A Survey of Anticipatory Mobile Networking: Context-Based Classification, Prediction Methodologies, and Optimization Techniques
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paper_title: Survey and Challenges of QoE Management Issues in Wireless Networks

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With the move towards converged all-IP wireless network environments, managing end-user Quality of Experience (QoE) poses a challenging task, aimed at meeting high user expectations and requirements regarding reliable and cost-effective communication, access to any service, anytime and anywhere, and across multiple operator domains. In this paper, we give a survey of state-of-the-art research activities addressing the field of QoE management, focusing in particular on the domain of wireless networks and addressing three management aspects: QoE modeling, monitoring and measurement, and adaptation and optimization. Furthermore, we identify and discuss the key aspects and challenges that need to be considered when conducting research in this area.
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paper_title: An Empirical Study of Throughput Prediction in Mobile Data Networks

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Bandwidth-sensitive applications such as adaptive video streaming rely on accurate prediction of future network throughput to enable them to react to and compensate for the rapidly fluctuating bandwidth often found in mobile networks. Researchers have developed various prediction algorithms in the literature of which many have been employed in real-world applications. However, there is a lack of systematic study on the comparative performance of the existing prediction algorithms in the context of mobile networks. This work addresses this void by conducting a systematic performance comparison of 7 prediction algorithms, and analyzes their characteristics when applied to the prediction of TCP throughput in mobile networks. The performance results are obtained from extensive trace-driven simulations where the throughput trace data were captured in production 3G/HSPA mobile networks in 3 locations over a period of 9 months and hence offer a good representation of the prediction algorithms' real- world performance. Furthermore, we applied the theory of differential entropy in information theory to obtain an estimated lower bound on throughput prediction errors which, for the first time, enables one to evaluate the absolute performance of these prediction algorithms. The results revealed that more complex algorithms are not necessarily better, and there exists a specific range of operating parameters where predictions are generally more accurate.
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paper_title: Anticipatory Mobile Computing: A Survey of the State of the Art and Research Challenges

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Today’s mobile phones are far from the mere communication devices they were 10 years ago. Equipped with sophisticated sensors and advanced computing hardware, phones can be used to infer users’ location, activity, social setting, and more. As devices become increasingly intelligent, their capabilities evolve beyond inferring context to predicting it, and then reasoning and acting upon the predicted context. This article provides an overview of the current state of the art in mobile sensing and context prediction paving the way for full-fledged anticipatory mobile computing. We present a survey of phenomena that mobile phones can infer and predict, and offer a description of machine learning techniques used for such predictions. We then discuss proactive decision making and decision delivery via the user-device feedback loop. Finally, we discuss the challenges and opportunities of anticipatory mobile computing.
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paper_title: A Survey of Measurement-Based Spectrum Occupancy Modeling for Cognitive Radios

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Spectrum occupancy models are very useful in cognitive radio designs. They can be used to increase spectrum sensing accuracy for more reliable operation, to remove spectrum sensing for higher resource usage efficiency, or to select channels for better opportunistic access, among other applications. In this survey, various spectrum occupancy models from measurement campaigns taken around the world are investigated. These models extract different statistical properties of the spectrum occupancy from the measured data. In addition to these models, spectrum occupancy prediction is also discussed, where autoregressive and/or moving-average models are used to predict the channel status at future time instants. After comparing these different methods and models, several challenges are also summarized based on this survey.
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paper_title: Understanding user behavior in online social networks: a survey

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Currently, online social networks such as Facebook, Twitter, Google+, LinkedIn, and Foursquare have become extremely popular all over the world and play a significant role in people?s daily lives. People access OSNs using both traditional desktop PCs and new emerging mobile devices. With more than one billion users worldwide, OSNs are a new venue of innovation with many challenging research problems. In this survey, we aim to give a comprehensive review of state-of-the-art research related to user behavior in OSNs from several perspectives. First, we discuss social connectivity and interaction among users. Also, we investigate traffic activity from a network perspective. Moreover, as mobile devices become a commodity, we pay attention to the characteristics of social behaviors in mobile environments. Last but not least, we review malicious behaviors of OSN users, and discuss several solutions to detect misbehaving users. Our survey serves the important roles of both providing a systematic exploration of existing research highlights and triggering various potentially significant research in these topics.
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paper_title: Spectrum Occupancy Measurements: A Survey and Use of Interference Maps

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In order to provide meaningful data about spectrum use, occupancy measurements describing the utilization rate of a specific frequency band should be conducted over a specific area instead of a single location. This paper presents a comprehensive methodology for the measurement and analysis of spectrum occupancy. This paper surveys spectrum measurement campaigns and associated interference maps, introducing the latter as a tool for spectrum analysis and management based on measurement data. An interference map characterizes the spectrum use by defining the level of interference over an area of interest in a certain frequency band. Building on findings from practical measurement studies, guidelines for spectrum occupancy measurements are given. While many scientific spectrum occupancy measurement papers tend to be too optimistic about the significance and generality of the results, we propose a cautionary perspective on drawing strong conclusions based on the often limited amount of data gathered. The different phases of the spectrum occupancy measurement and analysis process are described and a thorough discussion of interpolation methods is provided. Means to improve the measurement accuracy are discussed, especially regarding spatial domain considerations and the impact of the sampling interval on the results. A practical example of an improved measurement system design covering all the phases of the measurement process and used at the Turku, Finland; Blacksburg, VA, USA; and Chicago, IL, USA, spectrum observatories is given. Using the improved design, more realistic spectrum occupancy data can be obtained to lay the foundation for spectrum management decisions.
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paper_title: A survey of techniques for internet traffic classification using machine learning

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The research community has begun looking for IP traffic classification techniques that do not rely on `well known? TCP or UDP port numbers, or interpreting the contents of packet payloads. New work is emerging on the use of statistical traffic characteristics to assist in the identification and classification process. This survey paper looks at emerging research into the application of Machine Learning (ML) techniques to IP traffic classification - an inter-disciplinary blend of IP networking and data mining techniques. We provide context and motivation for the application of ML techniques to IP traffic classification, and review 18 significant works that cover the dominant period from 2004 to early 2007. These works are categorized and reviewed according to their choice of ML strategies and primary contributions to the literature. We also discuss a number of key requirements for the employment of ML-based traffic classifiers in operational IP networks, and qualitatively critique the extent to which the reviewed works meet these requirements. Open issues and challenges in the field are also discussed.
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paper_title: Big data-driven optimization for mobile networks toward 5G

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Big data offers a plethora of opportunities to mobile network operators for improving quality of service. This article explores various means of integrating big data analytics with network optimization toward the objective of improving the user quality of experience. We first propose a framework of Big Data-Driven (BDD) mobile network optimization. We then present the characteristics of big data that are collected not only from user equipment but also from mobile networks. Moreover, several techniques in data collection and analytics are discussed from the viewpoint of network optimization. Certain user cases on the application of the proposed framework for improving network performance are also given in order to demonstrate the feasibility of the framework. With the integration of the emerging fifth generation (5G) mobile networks with big data analytics, the quality of our daily mobile life is expected to be tremendously enhanced.
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paper_title: A Survey on Context-Aware Mobile and Wireless Networking: On Networking and Computing Environments' Integration

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During the last years, a continuous convergence and fusion of computing and networking systems is stressed in the ICT research community. Context awareness appears to be a major research "glue-point" of such kind of heterogeneous environments' integration. In this survey paper, we aim to assemble all the "puzzle-pieces" of the emerging research area of Context Aware Mobile and Wireless Networking (CAMoWiN) in order to provide the reader a holistic representation of the work having been done in the past emphasizing in recent research efforts. A precise definition of abstract Context Aware (CA) functionalities together with a clear classification of related state-of-the-art proposals are provided in the form of a proposed taxonomy scheme per CA functionality. Each taxonomy scheme can also serve as a sufficiently general framework permitting the easy classification of novel CAMoWiN solutions emerging in the near future. Therefore, our study not only defines all the possible components of a CAMoWiN architecture and clearly points out the similarities and differences of all research efforts in the field but also evaluates future research trends and paves the way for the researchers to work beyond the concepts investigated in this work. Finally, apart from identifying the open challenges, which arise when applying traditional concepts of pervasive computing to mobile and wireless networks, we further map them to the Mobile Cloud Computing paradigm, whose concepts appear to be very promising regarding next generation networking and computing environments' integration.
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paper_title: Approaching the Limit of Predictability in Human Mobility

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In this study we analyze the travel patterns of 500,000 individuals in Cote d'Ivoire using mobile phone call data records. By measuring the uncertainties of movements using entropy, considering both the frequencies and temporal correlations of individual trajectories, we find that the theoretical maximum predictability is as high as 88%. To verify whether such a theoretical limit can be approached, we implement a series of Markov chain (MC) based models to predict the actual locations visited by each user. Results show that MC models can produce a prediction accuracy of 87% for stationary trajectories and 95% for non-stationary trajectories. Our findings indicate that human mobility is highly dependent on historical behaviors, and that the maximum predictability is not only a fundamental theoretical limit for potential predictive power, but also an approachable target for actual prediction accuracy.
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paper_title: Big data-driven optimization for mobile networks toward 5G

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Big data offers a plethora of opportunities to mobile network operators for improving quality of service. This article explores various means of integrating big data analytics with network optimization toward the objective of improving the user quality of experience. We first propose a framework of Big Data-Driven (BDD) mobile network optimization. We then present the characteristics of big data that are collected not only from user equipment but also from mobile networks. Moreover, several techniques in data collection and analytics are discussed from the viewpoint of network optimization. Certain user cases on the application of the proposed framework for improving network performance are also given in order to demonstrate the feasibility of the framework. With the integration of the emerging fifth generation (5G) mobile networks with big data analytics, the quality of our daily mobile life is expected to be tremendously enhanced.
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paper_title: Predicting a user's next cell with supervised learning based on channel states

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Knowing a user's next cell allows more efficient resource allocation and enables new location-aware services. To anticipate the cell a user will hand-over to, we introduce a new machine learning based prediction system. Therein, we formulate the prediction as a classification problem based on information that is readily available in cellular networks. Using only Channel State Information (CSI) and handover history, we perform classification by embedding Support Vector Machines (SVMs) into an efficient pre-processing structure. Simulation results from a Manhattan Grid scenario and from a realistic radio map of downtown Frankfurt show that our system provides timely prediction at high accuracy.
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paper_title: Combining local and global profiles for mobility prediction in LTE femtocells

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We propose in this paper a mobility prediction model based on the notions of local and global mobile-user profiles. The local profiles are associated with a mobile user and correspond to its frequent and similar movements, whereas the global profiles match with the frequent and similar movements of the majority of users in the covered area. We consider the LTE network architecture with possible deployment of femtocells. The prediction model combines two complementary algorithms: the global profiles-based algorithm and the local profiles-based one. The former is implemented in the enhanced Node B and the home enhanced Node B and the latter works at the user terminal level. An algorithmic approach is used to identify such local and global profiles from real cellular network datasets and we show how to use them for an efficient mobility prediction. Simulation results show that our approach is significantly efficient in predicting both random and regular movements.
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paper_title: Mobility prediction-based smartphone energy optimization for everyday location monitoring

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Monitoring a user's mobility during daily life is an essential requirement in providing advanced mobile services. While extensive attempts have been made to monitor user mobility, previous work has rarely addressed issues with battery lifetime in real deployment. In this paper, we introduce SmartDC, a mobility prediction-based adaptive duty cycling scheme to provide contextual information about a user's mobility: time-resolved places and paths. Unlike previous approaches that focused on minimizing energy consumption for tracking raw coordinates, we propose efficient techniques to maximize the accuracy of monitoring meaningful places with a given energy constraint. SmartDC comprises unsupervised mobility learner, mobility predictor, and Markov decision process-based adaptive duty cycling. SmartDC estimates the regularity of individual mobility and predicts residence time at places to determine efficient sensing schedules. Our experiment results show that SmartDC consumes 81% less energy than the periodic sensing schemes, and 87% less energy than a scheme employing context-aware sensing, yet it still correctly monitors 80% of a user's location changes within a 160-second delay.
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paper_title: Mobility Prediction in Mobile Ad Hoc Networks Using Extreme Learning Machines

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Abstract Recent advances in wireless technology and computing have paved the way to the unprecedented rapid growth in de- mand and availability of mobile networking and services coupled with diverse system/network applications. Such advances triggered the emergence of future generation wireless networks and services to address the increasingly strin- gent requirements of quality-of-service (QoS) at various levels. The expected growth in wireless network activity and the number of wireless users will enable similar growth in bandwidth-crunching wireless applications to meet the QoS requirements. Mobility prediction of wireless users and units plays a major role in efficient planning and manage- ment of the bandwidth resources available in wireless networks. In return, this efficiency will allow better planning and improved overall QoS in terms of continuous service availability and efficient power management. In this paper, we propose extreme learning machines (ELMs), known for universal approximation, to model and predict mobility of arbitrary nodes in a mobile ad hoc network (MANET). MANETs use mobility prediction in location-aided routing and mobility aware topology control protocols. In these protocols, each mobile node is assumed to know its current mobility information (position, speed and movement direction angle). In this way, future node positions are predicted along with future distances between neighboring nodes. Unlike multilayer perceptrons (MLPs), ELMs capture better the existing interaction/correlation between the cartesian coordinates of the arbitrary nodes leading to more realistic and accurate mobility prediction based on several standard mobility models. Simulation results using standard mobility models illustrate how the proposed prediction method can lead to a significant improvement over conventional methods based on MLPs. Moreover, the proposed solution circumvents the prediction accuracy limitations in current algorithms when predicting future distances between neighboring nodes. The latter prediction is required by some applications like mobility aware topology control protocols.
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paper_title: Modeling steady-state and transient behaviors of user mobility: formulation, analysis, and application

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Recent studies on mobility modeling have focused on characterizing user mobility from real traces of wireless LANs (WLANs)and creating mobility models based on such characterization. However, most of the work does not study how user mobility is correlated in time at different time scales. For example, the future APs with which a user will be associated are predicted without the knowledge of when the association will take place and for how long. In this paper, we build a mathematical model for characterizing both steady state and transient behaviors of user mobility in WLANs. Specifically, we mode user mobility by a semi-Markov process, and obtain the transition probability matrix and the sojourn time distribution from the association history of WLAN users available at Dartmouth college [21]. With the steady-state characterization of user mobility in WLANs, we can estimate the long-term wireless network usage among different access points. By comparing the steady-state distributions of semi-Markov models built based on trace data collected at different time scales, we are able to characterize the degree of correlation in time and location.We also perform a transient behavior analysis of the semi-Markov process (that characterizes user mobility), and devise a timed location prediction algorithm that accurately predicts the future locations of users both the future access points they will associate themselves with and the association duration. We demonstrate the utility of timed location prediction, by showing how it can be utilized to predict the distribution of future user ocations with the time information figured in, and redistributing loads among neighboring APs. An improvement of 80% (in terms of load balance) is observed in a wide spectrum of traffic loads in the simulation.
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paper_title: Application of Mobility Prediction in Wireless Networks Using Markov Renewal Theory

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An understanding of the network traffic behavior is essential in the evolution of today's wireless networks and thus leads to a more efficient planning and management of the network's scarce bandwidth resources. Prior reservation of radio resources at future locations of a user's mobile trajectory can assist in optimizing the allocation of the network's limited resources and sustaining a desirable quality-of-service (QoS) level. This can also help to ensure that the network service can be available anywhere and anytime, which is only possible if, at any time, we can predict from where a user is going to make its demands. In this paper, we apply Markov renewal processes for both mobility modeling and predicting the likelihoods of the next-cell transition, along with anticipating the duration between the transitions, for an arbitrary user in a wireless network. Our proposed prediction technique will also be extended to compute the likelihoods of a user being in a particular state after N transitions. The proposed technique can also be used to estimate the expected spatial-temporal traffic load and activity at each location in a network's coverage area. Using some real traffic data, we illustrate how our proposed prediction method can lead to a significant improvement over some of the conventional methods.
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paper_title: Evaluating mobility models for temporal prediction with high-granularity mobility data

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A mobility model is an essential requirement in accurately predicting an individual's future location. While extensive studies have been conducted to predict human mobility, previous work used coarse-grained mobility data with limited ability to capture human movements at a fine-grained level. In this paper, we empirically analyze several mobility models for predicting temporal behavior of an individual user. Unlike previous approaches, which employed coarse-grained mobility data with partial temporal-coverage, we use fine-grained and continuous mobility data for the evaluation of mobility models.We explore the regularity and predictability of human mobility, and evaluate location-dependent and location-independent models with several feature-aided schemes. Our experimental results show that a location-dependent predictor is better than a location-independent predictor for predicting temporal behavior of individual user. The duration of stay at a location is strongly correlated to the arrival time at the current location and the return-tendency to the next location, rather than recent k location sequences.We also find that false-positive predictions can be reduced by adaptive use of mobility models.
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paper_title: SmartDC: Mobility Prediction-Based Adaptive Duty Cycling for Everyday Location Monitoring

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Monitoring a user's mobility during daily life is an essential requirement in providing advanced mobile services. While extensive attempts have been made to monitor user mobility, previous work has rarely addressed issues with predictions of temporal behavior in real deployment. In this paper, we introduce SmartDC, a mobility prediction-based adaptive duty cycling scheme to provide contextual information about a user's mobility: time-resolved places and paths. Unlike previous approaches that focused on minimizing energy consumption for tracking raw coordinates, we propose efficient techniques to maximize the accuracy of monitoring meaningful places with a given energy constraint. SmartDC comprises unsupervised mobility learner, mobility predictor, and Markov decision process-based adaptive duty cycling. SmartDC estimates the regularity of individual mobility and predicts residence time at places to determine efficient sensing schedules. Our experiment results show that SmartDC consumes 81 percent less energy than the periodic sensing schemes, and 87 percent less energy than a scheme employing context-aware sensing, yet it still correctly monitors 90 percent of a user's location changes within a 160-second delay.
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paper_title: Understanding the coverage and scalability of place-centric crowdsensing

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Crowd-enabled place-centric systems gather and reason over large mobile sensor datasets and target everyday user locations (such as stores, workplaces, and restaurants). Such systems are transforming various consumer services (for example, local search) and data-driven organizations (city planning). As the demand for these systems increases, our understanding of how to design and deploy successful crowdsensing systems must improve. In this paper, we present a systematic study of the coverage and scaling properties of place-centric crowdsensing. During a two-month deployment, we collected smartphone sensor data from 85 participants using a representative crowdsensing system that captures 48,000 different place visits. Our analysis of this dataset examines issues of core interest to place-centric crowdsensing, including place-temporal coverage, the relationship between the user population and coverage, privacy concerns, and the characterization of the collected data. Collectively, our findings provide valuable insights to guide the building of future place-centric crowdsensing systems and applications.
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paper_title: When and where next: individual mobility prediction

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The ability to predict when an individual mobile user will leave his current location and where we will move next enables a myriad of qualitatively different Location-Based Services (LBSes) and applications. To this extent, the present paper proposes a statistical method that explicitly performs these related temporal and spatial prediction tasks in three continuous, sequential phases. In the first phase, the method continuously extracts grid-based staytime statistics from the GPS coordinate stream of the location-aware mobile device of the user. In the second phase, from the grid-based staytime statistics, the method periodically extracts and manages regions that the user frequently visits. Finally, in the third phase, from the stream of region-visits, the method continuously estimates parameters for an inhomogeneous continuous-time Markov model and in a continuous fashion predicts when the user will leave his current region and where he will move next. Empirical evaluations, using a number of long, real world trajectories from the Geo-Life data set, show that the proposed method outperforms a state-of-the-art, rule-based trajectory predictor both in terms of temporal and spatial prediction accuracy.
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paper_title: MPaaS: Mobility prediction as a service in telecom cloud

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Mobile applications and services relying on mobility prediction have recently spurred lots of interest. In this paper, we propose mobility prediction based on cellular traces as an infrastructural level service of telecom cloud. Mobility Prediction as a Service (MPaaS) embeds mobility mining and forecasting algorithms into a cloud-based user location tracking framework. By empowering MPaaS, the hosted 3rd-party and value-added services can benefit from online mobility prediction. Particularly we took Mobility-aware Personalization and Predictive Resource Allocation as key features to elaborate how MPaaS drives new fashion of mobile cloud applications. Due to the randomness of human mobility patterns, mobility predicting remains a very challenging task in MPaaS research. Our preliminary study observed collective behavioral patterns (CBP) in mobility of crowds, and proposed a CBP-based mobility predictor. MPaaS system equips a hybrid predictor fusing both CBP-based scheme and Markov-based predictor to provide telecom cloud with large-scale mobility prediction capacity.
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paper_title: Tracking and prediction of mobility without physical distance measurements in sensor networks

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Existing methods for detection and tracking of mobile nodes and objects via sensor networks depend on physical localization of sensor nodes. Consequently, they suffer from disadvantages associated with localization, which is often costly, error prone, and in some cases even infeasible. Tracking mobile nodes in a topological coordinate domain is proposed as an alternative to that in geographic coordinate domain. Topological coordinates (TCs), the basis of recent developments in Topology Preserving Maps (TPMs), are derived from hop distances from each node to a small subset of nodes. A technique is presented to reduce the distortion closer to the edges of principal component based TPMs, thereby enhancing its accuracy, which in turn facilitates accurate mobility tracking. Topological Coordinate based Tracking and Prediction (TCTP) algorithm is proposed for tracking and predicting the position of mobile nodes in the TC domain. Simulation results presented show that tracking and detecting performance of TCTP is competitive with geographic coordinate based algorithms despite the fact that it relies only on hop distances to a subset of nodes.
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paper_title: Mobility Prediction Using Mobile User Profiles

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In this paper we propose a new approach for mobility prediction. It is based on the notion of mobile-user profile which corresponds to frequent similar movements of a user. Such a profile is defined, in the neighbourhood graph of a cellular network, as a set of similar sequences of crossed cells from one source cell to one destination cell. We propose an algorithmic approach to identify such profiles from real cellular network datasets and we show how to use them for an efficient mobility prediction. Simulation results show that the global approach is significantly efficient in predicting both random and regular movements.
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paper_title: Location patterns of mobile users: A large-scale tudy

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The opportunities to understand human-mobility have increased significantly of late with the rapid adoption of wireless devices that report locations frequently. In this work1, we utilize one such rich data-set comprising of nationwide call data records from several million users to analyze and understand their location patterns. We define a location pattern as the set of locations visited by a user, which roughly speaking, can be considered to be the footprint of the user. Such an analysis is useful since it allows insight into aspects such as the range covered by a user, general direction and major routes of travel, characterization of geographic areas etc.,. These in turn are useful inputs for network planning, traffic planning and mobility models. We propose a systematic methodology that utilizes geometric structures like the Minimum Area Rectangle, line segmentation and clustering techniques to extract meaningful information for location patterns and apply it to our large data-set. Based on this we report on aspects such as the size and orientation of footprints, length of major routes as well as characterize and compare locales based on movement patterns. Finally, we identify some key features of location patterns that can be modeled very well with a single statistical distribution, the Double Pareto LogNormal (DPLN) distribution regardless of locale.
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paper_title: Evaluating mobile signal and location predictability along public transportation routes

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Emerging mobility-aware content delivery ap- proaches are being proposed to cope with the increasing usage of data from vehicular users. The main idea is to forecast the user locations and associated link capacity, and then proactively counter service fluctuations in advance. For instance, a user that is heading towards low coverage can be prioritized and have video content prebuffered. While the reported gains are encouraging, the results are primarily based on assumptions of perfect prediction. Investigating the predictability of mobility and future signal variations is therefore imperative to evaluate the practical viability of such predictive content delivery paradigms. To this end, this paper presents a large-scale measurement study of 33 repeated trips along a 23.4km bus route covering urban and sub-urban areas in Kingston, Canada. We provide a thorough analysis of the collected traces to investigate the effects of geographical area, time, forecasting window, and contextual factors such as signal lights and bus stops. The collected dataset can also be used in several other ways to further investigate and drive research in predictive vehicular content delivery.
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paper_title: Route Prediction from Trip Observations

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This paper develops and tests algorithms for predicting the end-to-end route of a vehicle based on GPS observations of the vehicle’s past trips. We show that a large portion of a typical driver’s trips are repeated. Our algorithms exploit this fact for prediction by matching the first part of a driver’s current trip with one of the set of previously observed trips. Rather than predicting upcoming road segments, our focus is on making long term predictions of the route. We evaluate our algorithms using a large corpus of real world GPS driving data acquired from observing over 250 drivers for an average of 15.1 days per subject. Our results show how often and how accurately we can predict a driver’s route as a function of the distance already driven.
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paper_title: Broadcasting with Prediction and Selective Forwarding in Vehicular Networks

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Broadcasting in vehicular networks has attracted great interest in research community and industry. Broadcasting on disseminating information to individual vehicle beyond the transmission range is based on inter-vehicle communication systems. It is crucial to broadcast messages to other vehicles as fast as possible because the messages in vehicle communication systems are often emergency messages such as accident warning or alarm. In many current approaches, the message initiator or sender selects the node among its neighbors that is farthest away from it in the broadcasting direction and then assigns the node to rebroadcast the message once the node gets out of its range or after a particular time slot. However, this approach may select a nonoptimal candidate because it does not consider the moving status of vehicles including their moving directions and speeds. In this paper, we develop a new approach based on prediction of future velocity and selective forwarding. The current message sender selects the best candidate that will rebroadcast the message to other vehicles as fast as possible. Key to the decision making is to consider the candidates' previous moving status and predict the future moving trends of the candidates so that the message is spread out faster. In addition, this approach generates very low overhead. Simulations demonstrate that our approach significantly decreases end-to-end delay and improves message delivery ratio.
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paper_title: NextPlace: A Spatio-Temporal Prediction Framework for Pervasive Systems

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Accurate and fine-grained prediction of future user location and geographical profile has interesting and promising applications including targeted content service, advertisement dissemination for mobile users, and recreational social networking tools for smart-phones. Existing techniques based on linear and probabilistic models are not able to provide accurate prediction of the location patterns from a spatio-temporal perspective, especially for long-term estimation. More specifically, they are able to only forecast the next location of a user, but not his/her arrival time and residence time, i.e., the interval of time spent in that location. Moreover, these techniques are often based on prediction models that are not able to extend predictions further in the future. ::: ::: In this paper we present NextPlace, a novel approach to location prediction based on nonlinear time series analysis of the arrival and residence times of users in relevant places. NextPlace focuses on the predictability of single users when they visit their most important places, rather than on the transitions between different locations. We report about our evaluation using four different datasets and we compare our forecasting results to those obtained by means of the prediction techniques proposed in the literature. We show how we achieve higher performance compared to other predictors and also more stability over time, with an overall prediction precision of up to 90% and a performance increment of at least 50% with respect to the state of the art.
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paper_title: The predictive user mobility profile framework for wireless multimedia networks

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User mobility profile (UMP) is a combination of historic records and predictive patterns of mobile terminals, which serve as fundamental information for mobility management and enhancement of quality of service (QoS) in wireless multimedia networks. In this paper, a UMP framework is developed for estimating service patterns and tracking mobile users, including descriptions of location, mobility, and service requirements. For each mobile user, the service requirement is estimated using a mean-square error method. Moreover, a new mobility model is designed to characterize not only stochastic behaviors, but historical records and predictive future locations of mobile users as well. Therefore, our approach incorporates aggregate history and current system parameters to acquire UMP. In particular, an adaptive algorithm is designed to predict the future positions of mobile terminals in terms of location probabilities based on moving directions and residence time in a cell. Simulation results are shown to indicate that the proposed schemes are effective on mobility and resource management by evaluating blocking/dropping probabilities and location tracking costs in wireless networks.
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paper_title: Pattern Prediction and Passive Bandwidth Management for Hand-over Optimization in QoS Cellular Networks with Vehicular Mobility

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In wireless networking, the main desire of end-users is to take advantage of satisfactory services, in terms of QoS, especially when they pay for a required need. Many efforts have been made to investigate how the continuity of services can be guaranteed in QoS networks, where users can move from one cell to another one. The introduction of a prediction scheme with passive reservations is the only way to face this issue; however, the deployment of in-advance bandwidth leads the system to waste resources. This work consists of two main integrated contributions: a new pattern prediction scheme based on a distributed set of Markov chains, in order to handle passive reservations, and a statistical bandwidth management algorithm for the reduction of bandwidth wastage. The result of the integration is the Distributed Prediction with Bandwidth Management Algorithm (DPBMA) that is independent from the considered technology and the vehicular environment. Several simulation campaigns were conducted in order to evaluate the effectiveness of the proposed idea. It was also compared with other prediction schemes, in terms of system utilization, accuracy, call dropping, and call blocking probabilities.
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paper_title: WhereNext: a location predictor on trajectory pattern mining

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The pervasiveness of mobile devices and location based services is leading to an increasing volume of mobility data.This side eect provides the opportunity for innovative methods that analyse the behaviors of movements. In this paper we propose WhereNext, which is a method aimed at predicting with a certain level of accuracy the next location of a moving object. The prediction uses previously extracted movement patterns named Trajectory Patterns, which are a concise representation of behaviors of moving objects as sequences of regions frequently visited with a typical travel time. A decision tree, named T-pattern Tree, is built and evaluated with a formal training and test process. The tree is learned from the Trajectory Patterns that hold a certain area and it may be used as a predictor of the next location of a new trajectory finding the best matching path in the tree. Three dierent best matching methods to classify a new moving object are proposed and their impact on the quality of prediction is studied extensively. Using Trajectory Patterns as predictive rules has the following implications: (I) the learning depends on the movement of all available objects in a certain area instead of on the individual history of an object; (II) the prediction tree intrinsically contains the spatio-temporal properties that have emerged from the data and this allows us to define matching methods that striclty depend on the properties of such movements. In addition, we propose a set of other measures, that evaluate a priori the predictive power of a set of Trajectory Patterns. This measures were tuned on a real life case study. Finally, an exhaustive set of experiments and results on the real dataset are presented.
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paper_title: Interdependence and Predictability of Human Mobility and Social Interactions

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Previous studies have shown that human movement is predictable to a certain extent at different geographic scales. The existing prediction techniques exploit only the past history of the person taken into consideration as input of the predictors. In this paper, we show that by means of multivariate nonlinear time series prediction techniques it is possible to increase the forecasting accuracy by considering movements of friends, people, or more in general entities, with correlated mobility patterns (i.e., characterised by high mutual information) as inputs. Finally, we evaluate the proposed techniques on the Nokia Mobile Data Challenge and Cabspotting datasets.
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paper_title: Evaluating mobile signal and location predictability along public transportation routes

paper_content:

Emerging mobility-aware content delivery ap- proaches are being proposed to cope with the increasing usage of data from vehicular users. The main idea is to forecast the user locations and associated link capacity, and then proactively counter service fluctuations in advance. For instance, a user that is heading towards low coverage can be prioritized and have video content prebuffered. While the reported gains are encouraging, the results are primarily based on assumptions of perfect prediction. Investigating the predictability of mobility and future signal variations is therefore imperative to evaluate the practical viability of such predictive content delivery paradigms. To this end, this paper presents a large-scale measurement study of 33 repeated trips along a 23.4km bus route covering urban and sub-urban areas in Kingston, Canada. We provide a thorough analysis of the collected traces to investigate the effects of geographical area, time, forecasting window, and contextual factors such as signal lights and bus stops. The collected dataset can also be used in several other ways to further investigate and drive research in predictive vehicular content delivery.
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paper_title: Optimizing stored video delivery for mobile networks: The value of knowing the future

paper_content:

This paper considers the design of cross-layer opportunistic transport for stored video over wireless networks with a slow varying (average) capacity. We focus on two key ideas: (1) scheduling data transmissions when capacity is high; and (2), exploiting knowledge of future capacity variations. The latter is possible when users' mobility is known or predictable, e.g., users riding on public transportation or using navigation systems. We consider the design of cross-layer transmission schedules which minimize system utilization (and thus possibly transmit/receive energy) while avoiding, if at all possible, rebuffering/delays, in several scenarios. For the single-user anticipative case where all future capacity variations are known beforehand; we establish the optimal transmission schedule is a Generalized Piecewise Constant Thresholding (GPCT) scheme. For the single-user partially anticipative case where only a finite window of future capacity variations is known, we propose an online Greedy Fixed Horizon Control (GFHC). An upper bound on the competitive ratio of GFHC and GPCT is established showing how performance loss depends on the window size, receiver playback buffer, and capacity variability. Finally we consider the multiuser case where we can exploit both future temporal and multiuser diversity. Our simulations and evaluation based on a measured wireless capacity trace exhibit robust potential gains for our proposed transmission schemes.
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paper_title: GTube: geo-predictive video streaming over HTTP in mobile environments

paper_content:

Mobile video streaming sometimes suffers from playback interruptions which are typically due to considerable network bandwidth variations that a user may experience when s/he travels along a route. Segmented adaptive HTTP streaming that switches between video streams encoded at different bitrates -- and hence different quality levels -- can be used to alleviate the issues of variable bandwidth. An important issue with respect to users' perceived experience is how the system schedules the quality levels to match the end-to-end network bandwidth capacity. It is very beneficial if the application can estimate the future network conditions in advance, and therefore perform quality control and buffer control wisely. This work describes GTube, a video streaming system for receivers equipped with a GPS positioning sensor in mobile environments. The available network bandwidth for each location is collected by mobile users and then this data along with the measured locations are uploaded and recorded in a server. Mobile devices that stream video can send queries to the server in order to better predict the near-future bandwidth availability and plan quality adaptations accordingly.
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paper_title: Behavior-based mobility prediction for seamless handoffs in mobile wireless networks

paper_content:

The field of wireless networking has received unprecedented attention from the research community during the last decade due to its great potential to create new horizons for communicating beyond the Internet. Wireless LANs (WLANs) based on the IEEE 802.11 standard have become prevalent in public as well as residential areas, and their importance as an enabling technology will continue to grow for future pervasive computing applications. However, as their scale and complexity continue to grow, reducing handoff latency is particularly important. This paper presents the Behavior-based Mobility Prediction scheme to eliminate the scanning overhead incurred in IEEE 802.11 networks. This is achieved by considering not only location information but also group, time-of-day, and duration characteristics of mobile users. This captures short-term and periodic behavior of mobile users to provide accurate next-cell predictions. Our simulation study of a campus network and a municipal wireless network shows that the proposed method improves the next-cell prediction accuracy by 23~43% compared to location-only based schemes and reduces the average handoff delay down to 24~25 ms.
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paper_title: Channel gain prediction in wireless networks based on spatial-temporal correlation

paper_content:

Due to the popularity of GPS-enabled Smartphones the location of mobile terminals has become widely available [1]. Aided by such location information, we propose a general model to predict the channel gain of mobile users for multiple time steps in advance. Our model exploits spatial and temporal correlation in a Bayesian framework. The framework is composed of an autoregressive process and, according to our analysis of Rayleigh fading, of a multivariate Gaussian process. Numerical results shows that the proposed algorithm (i) achieves much higher accuracy than autoregression, especially for long-term prediction, and (ii) is substantially more robust than support vector machines against localization errors.
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paper_title: A Model for Throughput Prediction for Mobile Users

paper_content:

In this paper we propose a stochastic model to predict user throughput in mobile networks. In particular, the model accounts for uncertainty such as random phenomena (e.g., fast fading) or inexact information (e.g., user location) to derive the statistical distribution of the user throughput. Such a model is highly useful for aiding scheduling and resource allocation decisions. In addition, we provide a taxonomy of prediction techniques to investigate error sources and the main characteristics of prediction accuracy. ::: Finally, we show the versatility of the model by analyzing LTE user throughput for the case where knowledge of either the user's actual position or the congestion level in the cell is inexact.
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paper_title: Pattern Prediction and Passive Bandwidth Management for Hand-over Optimization in QoS Cellular Networks with Vehicular Mobility

paper_content:

In wireless networking, the main desire of end-users is to take advantage of satisfactory services, in terms of QoS, especially when they pay for a required need. Many efforts have been made to investigate how the continuity of services can be guaranteed in QoS networks, where users can move from one cell to another one. The introduction of a prediction scheme with passive reservations is the only way to face this issue; however, the deployment of in-advance bandwidth leads the system to waste resources. This work consists of two main integrated contributions: a new pattern prediction scheme based on a distributed set of Markov chains, in order to handle passive reservations, and a statistical bandwidth management algorithm for the reduction of bandwidth wastage. The result of the integration is the Distributed Prediction with Bandwidth Management Algorithm (DPBMA) that is independent from the considered technology and the vehicular environment. Several simulation campaigns were conducted in order to evaluate the effectiveness of the proposed idea. It was also compared with other prediction schemes, in terms of system utilization, accuracy, call dropping, and call blocking probabilities.
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paper_title: Video streaming using a location-based bandwidth-lookup service for bitrate planning

paper_content:

A lot of people around the world commute using public transportation and would like to spend this time viewing streamed video content such as news or sports updates. However, mobile wireless networks typically suffer from severe bandwidth fluctuations, and the networks are often completely unresponsive for several seconds, sometimes minutes. Today, there are several ways of adapting the video bitrate and thus the video quality to such fluctuations, for example, using scalable video codecs or segmented adaptive HTTP streaming that switches between nonscalable video streams encoded in different bitrates. Still, for a better long-term video playout experience that avoids disruptions and frequent quality changes while using existing video adaptation technology, it is desirable to perform bandwidth prediction and planned quality adaptation. This article describes a video streaming system for receivers equipped with a GPS. A receiver's download rate is constantly monitored, and periodically reported back to a central database along with associated GPS positional data. Thus, based on the current location, a streaming device can use a GPS-based bandwidth-lookup service in order to better predict the near-future bandwidth availability and create a schedule for the video playout that takes likely future availability into account. To create a prototype and perform initial tests, we conducted several field trials while commuting using public transportation. We show how our database has been used to predict bandwidth fluctuations and network outages, and how this information helps maintain uninterrupted playback with less compromise on video quality than possible without prediction.
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paper_title: Enhancing mobile data offloading with mobility prediction and prefetching

paper_content:

We present procedures that exploit mobility prediction and prefetching to enhance offloading of traffic from mobile networks to WiFi hotspots, for both delay tolerant and delay sensitive traffic. We evaluate the procedures in terms of the percentage of offloaded traffic, the data transfer delay, and the cache size used for prefetching. The evaluation considers empirical measurements and shows how various parameters influence the performance of the procedures, and their robustness to time and throughput estimation errors.
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paper_title: Mobility Prediction Using Mobile User Profiles

paper_content:

In this paper we propose a new approach for mobility prediction. It is based on the notion of mobile-user profile which corresponds to frequent similar movements of a user. Such a profile is defined, in the neighbourhood graph of a cellular network, as a set of similar sequences of crossed cells from one source cell to one destination cell. We propose an algorithmic approach to identify such profiles from real cellular network datasets and we show how to use them for an efficient mobility prediction. Simulation results show that the global approach is significantly efficient in predicting both random and regular movements.
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paper_title: Real-time bandwidth prediction and rate adaptation for video calls over cellular networks

paper_content:

We study interactive video calls between two users, where at least one of the users is connected over a cellular network. It is known that cellular links present highly-varying network bandwidth and packet delays. If the sending rate of the video call exceeds the available bandwidth, the video frames may be excessively delayed, destroying the interactivity of the video call. In this paper, we present Rebera, a cross-layer design of proactive congestion control, video encoding and rate adaptation, to maximize the video transmission rate while keeping the one-way frame delays sufficiently low. Rebera actively measures the available bandwidth in real-time by employing the video frames as packet trains. Using an online linear adaptive filter, Rebera makes a history-based prediction of the future capacity, and determines a bit budget for the video rate adaptation. Rebera uses the hierarchical-P video encoding structure to provide error resilience and to ease rate adaptation, while maintaining low encoding complexity and delay. Furthermore, Rebera decides in real time whether to send or discard an encoded frame, according to the budget, thereby preventing self-congestion and minimizing the packet delays. Our experiments with real cellular link traces demonstrate Rebera can, on average, deliver higher bandwidth utilization and shorter packet delays than Apple's FaceTime.
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paper_title: BreadCrumbs: forecasting mobile connectivity

paper_content:

Mobile devices cannot rely on a single managed network, but must exploit a wide variety of connectivity options as they travel. We argue that such systems must consider the derivative of connectivity--the changes inherent in movement between separately managed networks, with widely varying capabilities. With predictive knowledge of such changes, devices can more intelligently schedule network usage. To exploit the derivative of connectivity, we observe that people are creatures of habit; they take similar paths every day. Our system, BreadCrumbs, tracks the movement of the device's owner, and customizes a predictive mobility model for that specific user. Combined with past observations of wireless network capabilities, BreadCrumbs generates connectivity forecasts. We have built a BreadCrumbs prototype, and demonstrated its potential with several weeks of real-world usage. Our results show that these forecasts are sufficiently accurate, even with as little as one week of training, to provide improved performance with reduced power consumption for several applications.
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paper_title: Spatial Wireless Channel Prediction under Location Uncertainty

paper_content:

Spatial wireless channel prediction is important for future wireless networks, and in particular for proactive resource allocation at different layers of the protocol stack. Various sources of uncertainty must be accounted for during modeling and to provide robust predictions. We investigate two channel prediction frameworks, classical Gaussian processes (cGP) and uncertain Gaussian processes (uGP), and analyze the impact of location uncertainty during learning/training and prediction/testing, for scenarios where measurements uncertainty are dominated by large-scale fading. We observe that cGP generally fails both in terms of learning the channel parameters and in predicting the channel in the presence of location uncertainties.\textcolor{blue}{{} }In contrast, uGP explicitly considers the location uncertainty. Using simulated data, we show that uGP is able to learn and predict the wireless channel.
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paper_title: Path loss prediction in urban environment using learning machines and dimensionality reduction techniques

paper_content:

Path loss prediction is a crucial task for the planning of networks in modern mobile communication systems. Learning machine-based models seem to be a valid alternative to empirical and deterministic methods for predicting the propagation path loss. As learning machine performance depends on the number of input features, a good way to get a more reliable model can be to use techniques for reducing the dimensionality of the data. In this paper we propose a new approach combining learning machines and dimensionality reduction techniques. We report results on a real dataset showing the efficiency of the learning machine-based methodology and the usefulness of dimensionality reduction techniques in improving the prediction accuracy.
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paper_title: Anticipation of ETX Metric to Manage Mobility in Ad Hoc Wireless Networks

paper_content:

When a node is moving in a wireless network, the routing metrics associated to its wireless links may reflect link quality degradations and help the routing process to adapt its routes. Unfortunately, an important delay between the metric estimation and its inclusion in the routing process makes this approach inefficient. In this paper, we introduce an algorithm that predicts metric values a few seconds in advance, in order to compensate the delay involved by the link quality measurement and their dissemination by the routing protocol. We consider classical metrics, in particular ETX Expected Transmission Count and ETT Expected Transmission Time, but we combine their computations to our prediction algorithm. Extensive simulations show the route enhancement as the Packet Delivery Ratio PDR is close to 1 in presence of mobility.
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paper_title: Taming Wireless Fluctuations by Predictive Queuing Using a Sparse-Coding Link-State Model

paper_content:

We introduce State-Informed Link-Layer Queuing (SILQ), a system that models, predicts, and avoids packet delivery failures caused by temporary wireless outages in everyday scenarios. By stabilizing connections in adverse link conditions, SILQ boosts throughput and reduces performance variation for network applications, for example by preventing unnecessary TCP timeouts due to dead zones, elevators, and subway tunnels. SILQ makes predictions in real-time by actively probing links, matching measurements to an overcomplete dictionary of patterns learned offline, and classifying the resulting sparse feature vectors to identify those that precede outages. We use a clustering method called sparse coding to build our data-driven link model, and show that it produces more variation-tolerant predictions than traditional loss-rate, location-based, or Markov chain techniques. We present extensive data collection and field-validation of SILQ in airborne, indoor, and urban scenarios of practical interest. We show how offline unsupervised learning discovers link-state patterns that are stable across diverse networks and signal-propagation environments. Using these canonical primitives, we train outage predictors for 802.11 (Wi-Fi) and 3G cellular networks to demonstrate TCP throughput gains of 4x with off-the-shelf mobile devices. SILQ addresses delivery failures solely at the link layer, requires no new hardware, and upholds the end-to-end design principle, to enable easy integration across applications, devices, and networks.
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paper_title: (Not) yet another policy for scalable video delivery to mobile users

paper_content:

In this work, we provide a methodology to analyze optimal adaptation policies for scalable video delivery in mobile environments. Typically, download policies for adaptive video are tuned to very specific system settings. The aim of this work is not to propose a new policy, but instead to understand how the optimal policy changes according to the operating environment and the system characteristics of a mobile video client. Armed with this insight, we can design or adapt policies for SVC adaptive video delivery for a broader range of settings. Using a semi-Markov decision process (SMDP), we find optimal video retrieval policies for a single user, subject to different limits on buffer capacity and different wireless environments. We apply a decision tree classifier to the output of the SMDP to derive simple approximate policies for 55 scenarios and use these to derive high-level rules on the relationship between optimal download policy and the underlying channel settings. For example, we show that the optimal policy is more conservative in slowly varying channels, and becomes more greedy in fast changing channels, and that instantaneous channel state is relevant to the decision-making process only in a setting with a very limited buffer capacity and slow-varying channel.
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paper_title: On proactive caching with demand and channel uncertainties

paper_content:

Mobile data traffic has surpassed that of voice to become the main component of the system load of today's wireless networks. Recent studies indicate that the data demand patterns of mobile users are predictable. Moreover, the channel quality of mobile users along their navigation paths is predictable by exploiting their location information. This work aims at fusing the statistically predictable demand and channel patterns in devising proactive caching strategies that alleviate network congestion. Specifically, we establish a fundamental bound on the minimum possible cost achievable by any proactive scheduler under time-invariant demand and channel statistics as a function of their prediction uncertainties, and develop an asymptotically optimal proactive service policy that attains this bound as the prediction window grows. In addition, the established bound yields insights on how the demand and channel statistics affect proactive caching decisions. We reveal some of these insights through numerical investigations.
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paper_title: Bitrate adaptation for mobile video streaming based on buffer and channel state

paper_content:

Mobile video streaming is a rising business but poses significant challenges for network and service operators. In mobile networks, time-variant wireless channels often violate the rate requirement of the content, which leads to a stalled video stream. Common HTTP Adaptive Streaming techniques cannot effectively react to channel variation by adapting the video bitrate only to the state of the playback buffer. In this paper, we present an optimal solution that adapts to the current and predicted channel state as well. Our optimal formulation is based on Markov Decision Processes and studied with real content and channel traces from vehicular users. The results clearly show the large benefit of complementing buffer-aware by channel-aware adaptation and prediction. Finally, we present a heuristic that closely achieves optimal service quality at practical computational complexity.
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paper_title: Mobile network resource optimization under imperfect prediction

paper_content:

A highly interesting trend in mobile network optimization is to exploit knowledge of future network capacity to allow mobile terminals to prefetch data when signal quality is high and to refrain from communication when signal quality is low. While this approach offers remarkable benefits, it relies on the availability of a reliable forecast of system conditions. This paper focuses on the reliability of simple prediction techniques and their impact on resource allocation algorithms. In addition, we propose ICARO, a resource allocation technique that is robust to prediction uncertainties. The algorithm combines autoregressive filtering and statistical models for short, medium, and long term forecasting. We validate our approach by means of an extensive simulation campaign based on real measurement data collected in Berlin. We show that our solution performs close to an omniscient optimizer and outperforms a limited horizon omniscient optimizer by 10 – 15%. Our solution provides up to 30% saving of system resources compared to a simple solution that always maintains a full buffer and is close to optimal in terms of buffer under-run time.
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paper_title: On Managing Quality of Experience of Multiple Video Streams in Wireless Networks

paper_content:

Managing the Quality-of-Experience (QoE) of video streaming for wireless clients is becoming increasingly important due to the rapid growth of video traffic on wireless networks. The inherent variability of the wireless channel as well as the Variable Bit Rate (VBR) of the compressed video streams make QoE management a challenging problem. Prior work has studied this problem in the context of transmitting a single video stream. In this paper, we investigate multiplexing schemes to transmit multiple video streams from a base station to mobile clients that use number of playout stalls as a performance metric. In this context, we present an epoch-by-epoch framework to fairly allocate wireless transmission slots to streaming videos. In each epoch our scheme essentially reduces the vulnerability to stalling by allocating slots to videos in a way that maximizes the minimum ‘playout lead’ across all videos. Next, we show that the problem of allocating slots fairly is NP-complete even for a constant number of videos. We then present a fast lead-aware greedy algorithm for the problem. Our choice of greedy algorithm is motivated by the fact that this algorithm is optimal when the channel quality of a user remains unchanged within an epoch (but different users may experience different channel quality). Moreover, our experimental results based on public MPEG-4 video traces and wireless channel traces that we collected from a WiMAX test-bed show that the lead-aware greedy approach performs a fair distribution of stalls across the clients when compared to other algorithms, while still maintaining similar or lower average number of stalls per client.
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paper_title: Hop-by-hop adaptive video streaming in content centric network

paper_content:

To guarantee Quality of Experience (QoE) for video streaming services in a future Internet architecture, Content Centric Network (CCN), Dynamic Adaptive Streaming via HTTP (DASH) technology is used to deliver the proper video content according to the network situation. However, CCN enables a host-to-content communication model and has a universal caching design, which seriously decreases the performance of DASH over CCN. In this paper, we propose a hop-by-hop adaptive video streaming scheme (HAVS-CCN) to improve the performance of adaptive video streaming in CCN. HAVS-CCN is simple and applicable to be deployed on DASH over CCN. It directly adjusts video quality and solves network congestion at the bottleneck of transmission path when DASH inaccurately estimates the network throughout. Our scheme optimizes the hop-by-hop content transmission, which achieves video quality adaption and data packet flow control simultaneously. Simulation results, on small-scale networks and large-scale networks, reveal that DASH with HAVS-CCN scheme outperforms the original DASH over CCN, in terms of video playback quality and average delay.
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paper_title: Prediction-Based Throughput Optimization for Dynamic Spectrum Access

paper_content:

Cognitive radio (CR) for dynamic spectrum sensing and access has been a hot research topic in recent years. To avoid collision with the primary users, secondary users need to sense the channels before transmitting on them, which is referred to as sensing time overhead. Our previous work shows that the spectral correlations between the channels within the same service are sufficiently high for accurate prediction, which can further be used to reduce the sensing time. With such motivation, in this paper, we propose a new definition, i.e., channel availability vector (CAV), to characterize the state information of a group of licensed channels by introducing spectrum prediction while focusing on the scenario of a single secondary user with multiple channels and leverage it by formulating the throughput optimization problem as a Markov decision process, which is further solved by our optimal sensing scheme and verified with the real spectrum measurement data. The results show that our prediction-based sensing scheme outperforms one existing work.
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paper_title: Kernel-Based Adaptive Online Reconstruction of Coverage Maps With Side Information

paper_content:

In this paper, we address the problem of reconstructing coverage maps from path-loss measurements in cellular networks. We propose and evaluate two kernel-based adaptive online algorithms as an alternative to typical offline methods. The proposed algorithms are application-tailored extensions of powerful iterative methods such as the adaptive projected subgradient method (APSM) and a state-of-the-art adaptive multikernel method. Assuming that the moving trajectories of users are available, it is shown how side information can be incorporated in the algorithms to improve their convergence performance and the quality of the estimation. The complexity is significantly reduced by imposing sparsity awareness in the sense that the algorithms exploit the compressibility of the measurement data to reduce the amount of data that is saved and processed. Finally, we present extensive simulations based on realistic data to show that our algorithms provide fast and robust estimates of coverage maps in real-world scenarios. Envisioned applications include path-loss prediction along trajectories of mobile users as a building block for anticipatory buffering or traffic offloading.
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paper_title: Anticipatory Download Scheduling in Wireless Video Streaming with Uncertain Data Rate Prediction

paper_content:

Video streaming is in high demand by mobile users. In wireless networks the unreliable wireless channel leads to two major problems. Low available data rates degrade video quality and interrupt the playback when a user cannot sufficiently fill its local playout buffer: playback interruptions occur. In contrast, high available data rates cause common greedy buffering schemes to buffer too much data. Such over-buffering wastes wireless resources. Assuming that we can predict future data rates, we plan the quality and download time of video segments ahead. This anticipatory download scheduling avoids playback interruptions by downloading a large number of segments before a drop in available data rate occurs, without wasting wireless capacity by excessive buffering. In previous work we assumed a perfect prediction of future available data rates. Here we assume uncertain predictions, which are subject to errors and are only available for a limited future. We developed a stochastical model for prediction errors and implemented a new scheduling algorithm that is aware of prediction errors. Our results show that anticipatory download scheduling is feasible even with uncertain predictions and reduces playback interruptions while delivering a high video quality.
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paper_title: Joint Chance-Constrained Predictive Resource Allocation for Energy-Efficient Video Streaming

paper_content:

Predictive resource allocation (PRA) techniques that exploit knowledge of the future signal strength along roads have recently been recognized as promising approaches to save base station (BS) energy and improve user quality of service (QoS). Recent studies on human mobility patterns and wireless signal strength measurements along buses and trains have indeed supported the practical potential of PRA. An unresolved challenge, however, is modeling the uncertainty in the predictions, and developing real-time robust solutions that incorporate probabilistic QoS guarantees. This is of paramount importance in PRA due to the prediction time horizon that adds considerable complexity and increases the rate uncertainty in the problem. With these developments in mind, this paper addresses energy-efficient PRA applied to stored video streaming using chance constrained programming. The proposed solution incorporates: 1) uncertainty in predicted user rates; 2) a joint level of probabilistic constraint satisfaction over a time horizon; and 3) both optimal gradient-based and real-time guided heuristic solutions. Our framework fundamentally differs from previous PRA work in the literature where nonstochastic approaches with assumptions of perfect prediction were primarily used to demonstrate the potential energy savings and QoS gains. Numerical simulations based on a standard compliant long term evolution (LTE) system are provided to examine and compare the developed solution. Unlike existing energy-efficient PRA, the proposed framework achieves the desired QoS level under imperfect channel predictions. This robustness is attained without compromising the energy-efficiency compared to opportunistic schedulers, and thus supports PRA implementation in practice.
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paper_title: Robust resource allocation for predictive video streaming under channel uncertainty

paper_content:

Novel mobility-aware resource allocation schemes have recently been introduced for efficient transmission of stored videos. The essence of such mechanisms is to lookahead at the future rates users will experience, and then strategically buffer content into user devices when they are at peak radio conditions. For example, a user approaching poor coverage will be preallocated additional video segments to ensure smooth streaming. Advances in mobility prediction and real-time radio environment map updates are driving forces for such Predictive Video Streaming (PVS) mechanisms. Although previous efforts have demonstrated the large potential gains of PVS, ideal channel predictions were assumed. This paper addresses the problem of channel uncertainty in PVS, and proposes a robust resource allocation framework that 1) models channel uncertainty, 2) solves the PVS problem with a tunable level of quality of service guarantees, and 3) learns the degree of uncertainty, and adapts the channel model accordingly. Numerical results demonstrate the effectiveness of the proposed approach for PVS under channel variability.
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paper_title: A Model for Throughput Prediction for Mobile Users

paper_content:

In this paper we propose a stochastic model to predict user throughput in mobile networks. In particular, the model accounts for uncertainty such as random phenomena (e.g., fast fading) or inexact information (e.g., user location) to derive the statistical distribution of the user throughput. Such a model is highly useful for aiding scheduling and resource allocation decisions. In addition, we provide a taxonomy of prediction techniques to investigate error sources and the main characteristics of prediction accuracy. ::: Finally, we show the versatility of the model by analyzing LTE user throughput for the case where knowledge of either the user's actual position or the congestion level in the cell is inexact.
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paper_title: Chance-constrained QoS satisfaction for predictive video streaming

paper_content:

The promising energy saving and QoS gains of Predictive Resource Allocation (PRA) techniques have recently been recognized in the wireless network research community. These gains were primarily introduced in light of perfect prediction of both mobility traces and anticipated channel rates. However, under real world considerations of prediction errors, the reported gains cannot be guaranteed and further investigation is needed. In this paper, we demonstrate the practical potential of PRA by developing a robust, probabilistic framework that guarantees QoS satisfaction for video streaming under imperfect predictions, without compromising the energy saving gains. The proposed PRA framework uses chance-constrained programming to model video streaming QoS for all users during the foreseen time horizon. Closed form solutions are developed using the Gaussian and Bernstein approximations based on the channel statistical measures. Extensive numerical simulations using a standard compliant Long Term Evolution (LTE) system are presented to examine the developed solutions, for different user mobility scenarios and target QoS levels. The results demonstrate the various design trade-offs involved toward the practical deployment of predictive video streaming in future generation networks.
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paper_title: Link-level traffic scheduling for providing predictive QoS in wireless multimedia networks

paper_content:

A set of centralized burst-level cell scheduling schemes, namely, First Come First Served with Frame Reservation (FCFS-FR), FCFR-FR+, Earliest Deadline First with Frame Reservation (EDF-FR), EDF-FR+, and Multitraffic Dynamic Reservation (MTDR), are investigated for transmission of multiservice traffic over time division multiple access (TDMA)/time division duplex (TDD) channels in wireless ATM (WATM) networks. In these schemes, the number of time slots allocated to a virtual circuit (VC) during a frame-time is changed dynamically depending on the traffic type, system traffic load, the time of arrival (TOA)/time of expiry (TOE) value of the data burst and data burst length. The performances of these schemes are evaluated by computer simulation for realistic voice, video and data traffic models and their quality-of-service (QoS) requirements in a wireless mobile multimedia network. Both the error-free and the correlated fading channel conditions are considered. Simulation results show that the EDF-FR+ and MTDR schemes outperform the other schemes and can provide high channel utilization with predictive QoS guarantee in a multiservice traffic environment even in the presence of bursty channel errors. The EDF-FR+ scheme is found to provide better cell multiplexing performance than the MTDR scheme, Such a scheme would be easy to implement and would also result in a power conservative TDMA/TDD medium access control (MAC) protocol for broadband wireless access. Burst-level cell scheduling schemes such as EDF-FR+ can be easily adapted as MAC protocols in the emerging differentiated services (DS) enhanced wireless Internet protocol (IP) networks.
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paper_title: Video through a crystal ball: effect of bandwidth prediction quality on adaptive streaming in mobile environments

paper_content:

Mobile environments are characterized by rapidly fluctuating bandwidth and intermittent connectivity. Existing video streaming algorithms can perform poorly in such network conditions because of their reactive adaptation approach. Recent efforts suggest that bitrate adaptation using proactive accurate bandwidth prediction can help improve the quality of experience (QoE) of video streaming. However, highly accurate long-term predictions may be needed in mobile environments and those can be difficult to obtain. In this work, we examine the impact of bandwidth prediction quality on the QoE. We first characterize bandwidth profiles where bandwidth prediction-based adaptation can be useful. We then study the impact of prediction horizon and errors on the performance of Adaptive Bitrate (ABR) streaming. We observe that performance improves as the prediction horizon increases at first and then benefits start to diminish. We demonstrate that with proper error mitigation heuristic, even erroneous predictions can be useful in some scenarios. Finally, we study the role of video system parameters, namely buffer size and bitrate granularity on bandwidth prediction-based adaptation.
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paper_title: Anticipatory radio resource management for mobile video streaming with linear programming

paper_content:

In anticipatory networking, channel prediction is used to improve communication performance. This paper describes a new approach for allocating resources to video streaming traffic while accounting for quality of service. The proposed method is based on integrating a model of the user's local play-out buffer into the radio access network. The linearity of this model allows to formulate a Linear Programming problem that optimizes the trade-off between the allocated resources and the stalling time of the media stream. Our simulation results demonstrate the full power of anticipatory optimization in a simple, yet representative, scenario. Compared to instantaneous adaptation, our anticipatory solution shows impressive gains in spectral efficiency and stalling duration at feasible computation time while being robust against prediction errors.
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paper_title: Anticipatory resource allocation for wireless video streaming

paper_content:

This paper describes a new approach for allocating resources to video streaming traffic. Assuming that the future channel state can be predicted for a certain time, we minimize the fraction of the bandwidth consumed for smooth streaming by allocating wireless channel resources with respect to a constraint on the user's video play-out buffer. To formalize this idea, we introduce a new model to capture the dynamic of the play-out buffer and the allocated spectrum in an optimization problem. The result is a Linear Program that allows to trade off buffer size and allocated bandwidth. Based on this tractable model, our simulation results show that anticipating poor channel states and pre-loading the buffer accordingly allows to serve a high number of video users at increased spectral efficiency.
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paper_title: Energy-Efficient Adaptive Video Transmission: Exploiting Rate Predictions in Wireless Networks

paper_content:

The unprecedented growth of mobile video traffic is adding significant pressure to the energy drain at both the network and the end user. Energy-efficient video transmission techniques are thus imperative to cope with the challenge of satisfying user demand at sustainable costs. In this paper, we investigate how predicted user rates can be exploited for energy-efficient video streaming with the popular Hypertext Transfer Protocol (HTTP)-based adaptive streaming (AS) protocols [e.g., dynamic adaptive streaming over HTTP (DASH)]. To this end, we develop an energy-efficient predictive green streaming (PGS) optimization framework that leverages predictions of wireless data rates to achieve the following objectives: 1) Minimize the required transmission airtime without causing streaming interruptions; 2) minimize total downlink base station (BS) power consumption for cases where BSs can be switched off in deep sleep; and 3) enable a tradeoff between AS quality and energy consumption. Our framework is first formulated as mixed-integer linear programming (MILP) where decisions on multiuser rate allocation, video segment quality, and BS transmit power are jointly optimized. Then, to provide an online solution, we present a polynomial-time heuristic algorithm that decouples the PGS problem into multiple stages. We provide a performance analysis of the proposed methods by simulations, and numerical results demonstrate that the PGS framework yields significant energy savings.
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paper_title: Cross-layer scheduling for multi-quality video streaming in cellular wireless networks

paper_content:

Today's video delivery solutions for mobile terminals often use the HTTP Live Streaming (HLS) protocol, which has two interesting features: firstly, the video is divided into playable segments of a certain length, which allows to download and buffer segments before they are required for playback. And secondly, those segments can be available in different quality levels, which can be selected according to the available transmission capacity. Combining these features with scheduling and channel capacity prediction of wireless transmissions into a cross-layer scheduling approach could improve the QoE for users by reducing playback interruptions and by providing the best possible video quality depending on the user's wireless channel capacity. In this paper we propose a mixed integer quadratically constrained program (MIQCP) to implement the aforementioned cross-layer scheduling approach. We evaluate its performance compared to greedy strategies to assess the potential for a future polynomial time implementation.
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paper_title: SmarterPhones: Anticipatory download scheduling for wireless video streaming

paper_content:

Video streaming is in high demand by mobile users. In cellular networks, however, the unreliable wireless channel leads to two major problems. Poor channel states degrade video quality and interrupt the playback when a user cannot sufficiently fill its local playout buffer: buffer underruns occur. In contrast, good channel conditions cause common greedy buffering schemes to buffer too much data. Such over-buffering wastes expensive wireless channel capacity. Assuming that we can anticipate future data rates, we plan the quality and download time of video segments ahead. This anticipatory download scheduling avoids buffer underruns by downloading a large number of segments before a drop in available data rate occurs, without wasting wireless capacity by excessive buffering. We developed a practical anticipatory scheduling algorithm for segmented video streaming protocols (e.g., HLS or MPEG DASH). Simulation results and testbed measurements show that our solution essentially eliminates playback interruptions without significantly decreasing video quality.
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paper_title: Can Accurate Predictions Improve Video Streaming in Cellular Networks?

paper_content:

Existing video streaming algorithms use various estimation approaches to infer the inherently variable bandwidth in cellular networks, which often leads to reduced quality of experience (QoE). We ask the question: "If accurate bandwidth prediction were possible in a cellular network, how much can we improve video QoE?". Assuming we know the bandwidth for the entire video session, we show that existing streaming algorithms only achieve between 69%-86% of optimal quality. Since such knowledge may be impractical, we study algorithms that know the available bandwidth for a few seconds into the future. We observe that prediction alone is not sufficient and can in fact lead to degraded QoE. However, when combined with rate stabilization functions, prediction outperforms existing algorithms and reduces the gap with optimal to 4%. Our results lead us to believe that cellular operators and content providers can tremendously improve video QoE by predicting available bandwidth and sharing it through APIs.
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paper_title: Anticipatory Admission Control and Resource Allocation for Media Streaming in Mobile Networks

paper_content:

The exponential growth of media streaming traffic will have a strong impact on the bandwidth consumption of the future wireless infrastructure. One key challenge is to deliver services taking into account the stringent requirements of mobile video streaming, e.g., the users' expected Quality-of-Service. Admission control and resource allocation can strongly benefit from the use of anticipatory information such as the prediction of future user's demand and expected channel gain. In this paper, we use this information to formulate an optimal admission control scheme that maximizes the number of accepted users into the system with the constraint that not only the current but also the expected demand of all users must be satisfied. Together with the optimal set of accepted users, the optimal resource scheduling is derived. In order to have a solution that can be computed in a reasonable time, we propose a low complexity heuristic. Numerical results show the performance of the proposed scheme with respect to the state of the art.
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paper_title: Anticipatory quality-resource allocation for multi-user mobile video streaming

paper_content:

Mobile video delivery forms the largest part of the traffic in cellular networks. Thus optimizing the resource allocation to satisfy a user's quality of experience is becoming paramount in modern communications. This paper belongs to the line of research known as anticipatory networking that makes use of prediction of wireless capacity to improve communication performance. In particular, we focus on the problem of optimal resource allocation for steady video delivery under maximum average quality constraints for multiple users. We formulate the problem as a piecewise linear program and provide a heuristic algorithm, which solution is close to optimal. Based on our formulation we are now able to trade off minimum video quality, average quality and offered network capacity.
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paper_title: Efficient lookahead resource allocation for stored video delivery in multi-cell networks

paper_content:

Novel transmission mechanisms are imperatively needed to cope with the exponential growth of mobile traffic and its associated power consumption. To address such challenges, we present lookahead video delivery schemes that jointly improve the streaming experience and reduce BS power consumption. This is accomplished by exploiting knowledge of future wireless rates users are anticipated to face. Such an approach is useful for delivering stored videos that can be strategically buffered in advance at the users’ devices. For instance, a user leaving the cell center may have content prebuffered efficiently before poor channel conditions prevail. This will save energy as transmission will not be needed during poor conditions. In this paper, we first formulate Lookahead Resource Allocation (LRA) as a multi-cell optimization problem that leverages predictions of user mobility rates. Then, we present centralized and distributed algorithms that closely follow the benchmark results of the optimal solution. Numerical results demonstrate that significant improvements in video streaming and BS power consumption are achievable by the LRA strategies.
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paper_title: Opportunistic Channel Selection by Cognitive Wireless Nodes Under Imperfect Observations and Limited Memory: A Repeated Game Model

paper_content:

We study the problem of how autonomous cognitive nodes (CNs) can arrive at an efficient and fair opportunistic channel access policy in scenarios where channels may be non-homogeneous in terms of primary user (PU) occupancy. In our model, a CN that is able to adapt to the environment is limited in two ways. First, CNs have imperfect observations (such as due to sensing and channel errors) of their environment. Second, CNs have imperfect memory due to limitations in computational capabilities. For efficient opportunistic channel access, we propose a simple adaptive win-shift lose-randomize (WSLR) strategy that can be executed by a two-state machine (automaton). Using the framework of repeated games (with imperfect observations and limited memory), we show that the proposed strategy enables the CNs (without any explicit coordination) to reach an outcome that: 1) maximizes the total network payoff and also ensures fairness among the CNs; 2) reduces the likelihood of collisions among CNs; and 3) requires a small number of sensing steps (attempts) to find a channel free of PU activity. We compare the performance of the proposed autonomous strategy with a centralized strategy and also test it with real spectrum data collected at RWTH Aachen.
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paper_title: A Survey of Measurement-Based Spectrum Occupancy Modeling for Cognitive Radios

paper_content:

Spectrum occupancy models are very useful in cognitive radio designs. They can be used to increase spectrum sensing accuracy for more reliable operation, to remove spectrum sensing for higher resource usage efficiency, or to select channels for better opportunistic access, among other applications. In this survey, various spectrum occupancy models from measurement campaigns taken around the world are investigated. These models extract different statistical properties of the spectrum occupancy from the measured data. In addition to these models, spectrum occupancy prediction is also discussed, where autoregressive and/or moving-average models are used to predict the channel status at future time instants. After comparing these different methods and models, several challenges are also summarized based on this survey.
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paper_title: Cognitive Radio for Smart Grids: Survey of Architectures, Spectrum Sensing Mechanisms, and Networking Protocols

paper_content:

Traditional power grids are currently being transformed into smart grids (SGs). SGs feature multi-way communication among energy generation, transmission, distribution, and usage facilities. The reliable, efficient, and intelligent management of complex power systems requires integration of high-speed, reliable, and secure data information and communication technology into the SGs to monitor and regulate power generation and usage. Despite several challenges, such as trade-offs between wireless coverage and capacity as well as limited spectral resources in SGs, wireless communication is a promising SG communications technology. Cognitive radio networks (CRNs) in particular are highly promising for providing timely SG wireless communications by utilizing all available spectrum resources. We provide in this paper a comprehensive survey on the CRN communication paradigm in SGs, including the system architecture, communication network compositions, applications, and CR-based communication technologies. We highlight potential applications of CR-based SG systems. We survey CR-based spectrum sensing approaches with their major classifications. We also provide a survey on CR-based routing and MAC protocols, and describe interference mitigation schemes. We furthermore present open issues and research challenges faced by CR-based SG networks along with future directions.
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paper_title: Characterizing feasible interference region for underlay cognitive radio networks

paper_content:

In an underlay cognitive radio network (CRN), in order to guarantee protection of primary users (PUs) (i.e., all PUs achieve their target signal-to-interference-plus-noise ratios [SINRs]), the interference caused by all secondary users (SUs) to the primary receiving-points should be controlled. To do so, the feasible cognitive interference region (FCIR), i.e., the region for allowable values of interference at all of the primary receiving-points, which guarantee protecting the PUs, needs to be formally characterized. In the state-of-the-art interference management schemes for underlay CRNs, it is considered that all PUs are protected if the cognitive interference for each primary receiving-point is lower than a maximum threshold, the so called interference temperature limit (ITL) for the corresponding receiving-point. This is assumed to be fixed and independent of ITL values for other primary receiving-points. This corresponds to a box-like FCIR, which is not correct. In this paper, we analytically obtain the FCIR and show that, the FCIR is a polyhedron (i.e., the maximum feasible cognitive interference threshold for each primary receiving-point is not constant, and it depends on that for other primary receiving-points). Therefore, in practical interference management algorithms, it is not proper to consider a constant and independent ITL value for each of the primary receiving-points. This finding would significantly affect the design of practical interference management schemes for CRNs.
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paper_title: On the Construction of Radio Environment Maps for Cognitive Radio Networks

paper_content:

The Radio Environment Map (REM) provides an effective approach to Dynamic Spectrum Access (DSA) in Cognitive Radio Networks (CRNs). Previous results on REM construction show that there exists a tradeoff between the number of measurements (sensors) and REM accuracy. In this paper, we analyze this tradeoff and determine that the REM error is a decreasing and convex function of the number of measurements (sensors). The concept of geographic entropy is introduced to quantify this relationship. And the influence of sensor deployment on REM accuracy is examined using information theory techniques. The results obtained in this paper are applicable not only for the REM, but also for wireless sensor network deployment.
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paper_title: On the Utilization of Spectrum Opportunity in Cognitive Radio Networks

paper_content:

Cognitive radio (CR) is proposed to use the spectrum opportunistically. Spectrum opportunity (SOP) can be defined as the possibility of a spectrum-aware communication between a CR transmitter and a CR receiver. Successful spectrum-aware communication between the communicating CRs, which is utilization of SOP (USOP), depends on the SOP detection and the correct transmission of a packet. Spectrum sensing performance, physical channel, and network parameters affect the probability of USOP. In this letter, we characterize the probability of the USOP under different network topologies. The network topology is due to the relation between transmission ranges of licensed users and CRs. We numerically study this probability for different network parameters and topologies. We find that the characteristics of USOP highly depend on the network topology, CR sensing performance and licensed users’ activities.
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paper_title: On Characterization of Feasible Interference Regions in Cognitive Radio Networks

paper_content:

In an underlay cognitive radio network (CRN), in order to guarantee that all primary users (PUs) achieve their target-signal-to-interference-plus-noise ratios (target-SINRs), the interference caused by all secondary users (SUs) to the primary receiving-points should be controlled. To do so, the feasible cognitive interference region (FCIR), i.e., the region for allowable values of interference at all of the primary receiving-points, which guarantee the protection of the PUs, needs to be formally characterized. In the state-of-the-art interference management schemes for underlay CRNs, it is considered that all PUs are protected if the cognitive interference for each primary receiving-point is lower than a maximum threshold, the so called interference temperature limit (ITL) for the corresponding receiving-point. This is assumed to be fixed and independent of ITL values for other primary receiving-points, which corresponds to a box-like FCIR. In this paper, we characterize the FCIR for uplink transmissions in cellular CRNs and for direct transmissions in ad-hoc CRNs. We show that the FCIR is in fact a polyhedron (i.e., the maximum feasible cognitive interference threshold for each primary receiving-point is not a constant, and it depends on that for the other primary receiving-points). Therefore, in practical interference management algorithms, it is not proper to consider a constant and independent ITL value for each of the primary receiving-points. This finding would significantly affect the design of practical interference management schemes for CRNs. To demonstrate this, based on the characterized FCIR, we propose two power control algorithms to find the maximum number of admitted SUs and the maximum aggregate throughput of the SUs in infeasible and feasible CRNs, respectively. For two distinct objectives, our proposed interference management schemes outperform the existing ones. The numerical results also demonstrate how the assumption of fixed ITL values leads to poor performance measures in CRNs.
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paper_title: Spectrum Occupancy Measurements: A Survey and Use of Interference Maps

paper_content:

In order to provide meaningful data about spectrum use, occupancy measurements describing the utilization rate of a specific frequency band should be conducted over a specific area instead of a single location. This paper presents a comprehensive methodology for the measurement and analysis of spectrum occupancy. This paper surveys spectrum measurement campaigns and associated interference maps, introducing the latter as a tool for spectrum analysis and management based on measurement data. An interference map characterizes the spectrum use by defining the level of interference over an area of interest in a certain frequency band. Building on findings from practical measurement studies, guidelines for spectrum occupancy measurements are given. While many scientific spectrum occupancy measurement papers tend to be too optimistic about the significance and generality of the results, we propose a cautionary perspective on drawing strong conclusions based on the often limited amount of data gathered. The different phases of the spectrum occupancy measurement and analysis process are described and a thorough discussion of interpolation methods is provided. Means to improve the measurement accuracy are discussed, especially regarding spatial domain considerations and the impact of the sampling interval on the results. A practical example of an improved measurement system design covering all the phases of the measurement process and used at the Turku, Finland; Blacksburg, VA, USA; and Chicago, IL, USA, spectrum observatories is given. Using the improved design, more realistic spectrum occupancy data can be obtained to lay the foundation for spectrum management decisions.
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paper_title: Primary radio user activity models for cognitive radio networks: A survey

paper_content:

Abstract Cognitive Radio Networks have been emerged as a promising solution for solving the problem of spectrum scarcity and improving spectrum utilization by opportunistic use of spectrum. Cognitive radio networks utilize the spectrum which is licensed to primary radio users when they are not utilizing it, i.e., when the spectrum is idle. Thus, the performance of cognitive radio networks is highly dependent upon the activity of primary radio users. Hence, it is very important to model primary radio users activity in cognitive radio networks. By keeping this in mind, several models in the literature have been proposed for modeling primary radio users activity. But there is not any source which consolidate all these models into single platform. Therefore, this paper combines all the primary radio user activity models for cognitive radio networks at a single place. The goal of this paper is to provide a single source in the form of survey paper by which a reader can get an idea about which primary radio user activity models have been used in the literature for cognitive radio networks and how the modeling is performed. Furthermore, we also discuss issues, challenges and future directions for primary radio activity models. In fact, in this paper, different primary radio user activity models have been presented along with their classification. This paper also discusses those approaches which performed real implementation for spectrum occupancy along with spectrum bands on which the implementation is performed and location where implementation is carried out. In summary, this paper provides up-to-date survey of primary radio user activity models for cognitive radio networks.
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paper_title: Machine Learning Techniques for Cooperative Spectrum Sensing in Cognitive Radio Networks

paper_content:

We propose novel cooperative spectrum sensing (CSS) algorithms for cognitive radio (CR) networks based on machine learning techniques which are used for pattern classification. In this regard, unsupervised (e.g., K-means clustering and Gaussian mixture model (GMM)) and supervised (e.g., support vector machine (SVM) and weighted K-nearest-neighbor (KNN)) learning-based classification techniques are implemented for CSS. For a radio channel, the vector of the energy levels estimated at CR devices is treated as a feature vector and fed into a classifier to decide whether the channel is available or not. The classifier categorizes each feature vector into either of the two classes, namely, the "channel available class" and the "channel unavailable class". Prior to the online classification, the classifier needs to go through a training phase. For classification, the K-means clustering algorithm partitions the training feature vectors into K clusters, where each cluster corresponds to a combined state of primary users (PUs) and then the classifier determines the class the test energy vector belongs to. The GMM obtains a mixture of Gaussian density functions that well describes the training feature vectors. In the case of the SVM, the support vectors (i.e., a subset of training vectors which fully specify the decision function) are obtained by maximizing the margin between the separating hyperplane and the training feature vectors. Furthermore, the weighted KNN classification technique is proposed for CSS for which the weight of each feature vector is calculated by evaluating the area under the receiver operating characteristic (ROC) curve of that feature vector. The performance of each classification technique is quantified in terms of the average training time, the sample classification delay, and the ROC curve. Our comparative results clearly reveal that the proposed algorithms outperform the existing state-of-the-art CSS techniques.
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paper_title: A Survey of Channel Bonding for Wireless Networks and Guidelines of Channel Bonding for Futuristic Cognitive Radio Sensor Networks

paper_content:

Channel bonding (CB) is a proven technique to increase bandwidth and reduce delays in wireless networks. It has been applied in traditional wireless networks such as cellular networks and wireless local area networks along with the emerging cognitive radio networks. This paper first focuses on providing a survey of CB schemes for traditional wireless networks such as cellular networks, wireless local area networks, and wireless sensor networks, and then provides a detailed discussion on the CB schemes proposed for cognitive radio networks. Finally, we highlight a number of issues and challenges regarding CB in cognitive radio sensor networks and also provide some guidelines on using CB schemes in these futuristic networks.
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paper_title: Predictive Green Wireless Access: Exploiting Mobility and Application Information

paper_content:

The ever increasing mobile data traffic and dense deployment of wireless networks have made energy efficient radio access imperative. As networks are designed to satisfy peak user demands, radio access energy can be reduced in a number of ways at times of lower demand. This includes putting base stations (BSs) to intermittent short sleep modes during low load, as well as adaptively powering down select BSs completely where demand is low for prolonged time periods. In order to fully exploit such energy conserving mechanisms, networks should be aware of the user temporal and spatial traffic demands. To this end, this article investigates the potential of utilizing predictions of user location and application information as a means to energy saving. We discuss the development of a predictive green wireless access (PreGWA) framework and identify its key functional entities and their interaction. To demonstrate the potential energy savings we then provide a case study on stored video streaming and illustrate how exploiting predictions can minimize BS resource consumption within a single cell, and across a network of cells. Finally, to emphasize the practical potential of PreGWA, we present a distributed heuristic that reduces resource consumption significantly without requiring considerable information or signaling overhead.
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paper_title: PROTEUS: network performance forecast for real-time, interactive mobile applications

paper_content:

Real-time communication (RTC) applications such as VoIP, video conferencing, and online gaming are flourishing. To adapt and deliver good performance, these applications require accurate estimations of short-term network performance metrics, e.g., loss rate, one-way delay, and throughput. However, the wide variation in mobile cellular network performance makes running RTC applications on these networks problematic. To address this issue, various performance adaptation techniques have been proposed, but one common problem of such techniques is that they only adjust application behavior reactively after performance degradation is visible. Thus, proactive adaptation based on accurate short-term, fine-grained network performance prediction can be a preferred alternative that benefits RTC applications. In this study, we show that forecasting the short-term performance in cellular networks is possible in part due to the channel estimation scheme on the device and the radio resource scheduling algorithm at the base station. We develop a system interface called PROTEUS, which passively collects current network performance, such as throughput, loss, and one-way delay, and then uses regression trees to forecast future network performance. PROTEUS successfully predicts the occurrence of packet loss within a 0.5s time window for 98% of the time windows and the occurrence of long one-way delay for 97% of the time windows. We also demonstrate how PROTEUS can be integrated with RTC applications to significantly improve the perceptual quality. In particular, we increase the peak signal-to-noise ratio of a video conferencing application by up to 15dB and reduce the perceptual delay in a gaming application by up to 4s.
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paper_title: Power-Delay Tradeoff With Predictive Scheduling in Integrated Cellular and Wi-Fi Networks

paper_content:

The explosive growth of global mobile traffic has led to rapid growth in the energy consumption in communication networks. In this paper, we focus on the energy-aware design of the network selection, subchannel, and power allocation in cellular and Wi-Fi networks, while taking into account the traffic delay of mobile users. Based on the two-timescale Lyapunov optimization technique, we first design an online Energy-Aware Network Selection and Resource Allocation (ENSRA) algorithm, which yields a power consumption within $O\left({\frac{1}{V}} \right)$ bound of the optimal value, and guarantees an $O\left(V \right)$ traffic delay for any positive control parameter $V$ . Motivated by the recent advancement in the accurate estimation and prediction of user mobility, channel conditions, and traffic demands, we further develop a novel predictive Lyapunov optimization technique to utilize the predictive information, and propose a Predictive Energy-Aware Network Selection and Resource Allocation (P-ENSRA) algorithm. We characterize the performance bounds of P-ENSRA in terms of the power-delay tradeoff theoretically. To reduce the computational complexity, we finally propose a Greedy Predictive Energy-Aware Network Selection and Resource Allocation (GP-ENSRA) algorithm, where the operator solves the problem in P-ENSRA approximately and iteratively. Numerical results show that GP-ENSRA significantly improves the power-delay performance over ENSRA in the large delay regime. For a wide range of system parameters, GP-ENSRA reduces the traffic delay over ENSRA by 20–30% under the same power consumption.
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paper_title: CS2P: Improving Video Bitrate Selection and Adaptation with Data-Driven Throughput Prediction

paper_content:

Bitrate adaptation is critical in ensuring good users’ quality-of-experience (QoE) in Internet video delivery system. Several efforts have argued that accurate throughput prediction can dramatically improve (1) initial bitrate selection for low startup delay and high initial resolution; (2) midstream bitrate adaptation for high QoE. However, prior ef- forts did not systematically quantify real-world throughput predictability or develop good prediction algorithms. To bridge this gap, this paper makes three key technical contributions: First, we analyze the throughput characteristics in a dataset with 20M+ sessions. We find: (a) Sessions sharing similar key features (e.g., ISP, region) present similar initial values and dynamical patterns; (b) There is a natural “stateful” dynamical behavior within a given session. Second, building on these insights, we develop CS2P, a better throughput prediction system. CS2P leverages data-driven approach to learn (a) clusters of similar sessions, (b) an initial throughput predictor, and (c) a Hidden-Markov-Model based midstream predictor modeling the stateful evolution of throughput. Third, we develop a prototype system and show by trace-driven simulation and real-world experiments that CS2P outperforms state-of-art by 40% and 50% median pre- diction error respectively for initial and midstream through- put and improves QoE by 14% over buffer-based adaptation algorithm.
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paper_title: Proactive Resource Allocation: Harnessing the Diversity and Multicast Gains

paper_content:

This paper introduces the novel concept of proactive resource allocation for wireless networks, through which the predictability of user behavior is exploited to balance the wireless traffic over time, and significantly reduces the bandwidth required to achieve a given blocking/outage probability. We start with a simple model in which smart wireless devices are assumed to predict the arrival of new requests and submit them to the network time slots in advance. Using tools from large deviation theory, we quantify the resulting prediction diversity gain to establish that the decay rate of the outage event probabilities increases with the prediction duration . Remarkably, we also show that, in the cognitive networking scenario, the appropriate use of proactive resource allocation by primary users improves the diversity gain of the secondary network at no cost in the primary network diversity. We also shed light on multicasting with predictable demands and show that proactive multicast networks can achieve a significantly higher diversity gain that scales superlinearly with . Finally, we conclude by a discussion of the new research questions posed under the umbrella of the proposed proactive wireless resource framework.
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paper_title: Multi-scale high-speed network traffic prediction using k-factor Gegenbauer ARMA model

paper_content:

Gegenbauer autoregressive moving average (GARMA) model has the ability to capture both the short- and long-range dependent characteristics of the underlying data. GARMA has been used for modeling and forecasting of the financial time series that exhibits a long-range dependency (LRD). Since the high-speed network traffic exhibits a high degree of LRD characteristic, GARMA could be used for its modeling and prediction. In this paper, we present a simplified parameter estimation procedure and an adaptive prediction scheme for the k-factor GARMA model. The adaptation gives the model the ability to capture, the non-stationary characteristic of the data. The k-factor GARMA is applied to model four different types of real traffic data: MPEG and JPEG video, Ethernet and Internet. These models are then used to predict one-step-ahead traffic value at different timescales. The results show that the estimated parameters of the k-factor GARMA model provide a detailed and accurate presentation for the traffic characteristics in both time and frequency domain. We also demonstrate that the prediction performance of the k-factor GARMA model outperforms that of the traditional autoregressive (AR) model.
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paper_title: A Control-Theoretic Approach to Adaptive Video Streaming in Dense Wireless Networks

paper_content:

Recently, the way people consume video content has been undergoing a dramatic change. Plain TV sets, that have been the center of home entertainment for a long time, are losing ground to hybrid TVs, PCs, game consoles, and, more recently, mobile devices such as tablets and smartphones. The new predominant paradigm is: watch what I want, when I want, and where I want. The challenges of this shift are manifold. On the one hand, broadcast technologies such as DVB-T/C/S need to be extended or replaced by mechanisms supporting asynchronous viewing, such as IPTV and video streaming over best-effort networks, while remaining scalable to millions of users. On the other hand, the dramatic increase of wireless data traffic begins to stretch the capabilities of the existing wireless infrastructure to its limits. Finally, there is a challenge to video streaming technologies to cope with a high heterogeneity of end-user devices and dynamically changing network conditions, in particular in wireless and mobile networks. In the present work, our goal is to design an efficient system that supports a high number of unicast streaming sessions in a dense wireless access network. We address this goal by jointly considering the two problems of wireless transmission scheduling and video quality adaptation, using techniques inspired by the robustness and simplicity of proportional-integral-derivative (PID) controllers. We show that the control-theoretic approach allows to efficiently utilize available wireless resources, providing high quality of experience (QoE) to a large number of users.
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paper_title: Long-term forecasting of Internet backbone traffic: observations and initial models

paper_content:

We introduce a methodology to predict when and where link additions/upgrades have to take place in an IP backbone network. Using SNMP statistics, collected continuously since 1999, we compute aggregate demand between any two adjacent PoPs and look at its evolution at time scales larger than one hour. We show that IP backbone traffic exhibits visible long term trends, strong periodicities, and variability at multiple time scales. Our methodology relies on the wavelet multiresolution analysis and linear time series models. Using wavelet multiresolution analysis, we smooth the collected measurements until we identify the overall long-term trend. The fluctuations around the obtained trend are further analyzed at multiple time scales. We show that the largest amount of variability in the original signal is due to its fluctuations at the 12 hour time scale. We model inter-PoP aggregate demand as a multiple linear regression model, consisting of the two identified components. We show that this model accounts for 98% of the total energy in the original signal, while explaining 90% of its variance. Weekly approximations of those components can be accurately modeled with low-order autoregressive integrated moving average (ARIMA) models. We show that forecasting the long term trend and the fluctuations of the traffic at the 12 hour time scale yields accurate estimates for at least six months in the future.
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paper_title: Characterizing and modeling internet traffic dynamics of cellular devices

paper_content:

Understanding Internet traffic dynamics in large cellular networks is important for network design, troubleshooting, performance evaluation, and optimization. In this paper, we present the results from our study, which is based upon a week-long aggregated flow level mobile device traffic data collected from a major cellular operator's core network. In this study, we measure and characterize the spatial and temporal dynamics of mobile Internet traffic. We distinguish our study from other related work by conducting the measurement at a larger scale and exploring mobile data traffic patterns along two new dimensions -- device types and applications that generate such traffic patterns. Based on the findings of our measurement analysis, we propose a Zipf-like model to capture the volume distribution of application traffic and a Markov model to capture the volume dynamics of aggregate Internet traffic. We further customize our models for different device types using an unsupervised clustering algorithm to improve prediction accuracy.
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paper_title: Generalized Predictive Control in a Wireless Networked Control System

paper_content:

The NCS (networked control system) is different from the conventional control systems which is the integration of the automation and control over communication network. When an NCS operates over the communication network, one of the major challenges is the network-induced delay in data transfer among the controllers, actuators, and sensors. This delay degrades system performance and causes system unstablility. This paper proposes a GPC (generalized predictive control) with the Kalman state estimator to compensate for the network-induced delay and packet loss. The GPC is implemented in WiNCS (Wireless NCS) based on IEEE 802.11 standard. An analytical NCS model and NS2 (network simulator version 2) are developed to simulate and evaluate the performance under the effect of various delays and packet loss rates. The result shows that the proposed GPC is adaptive and robust to the uncertainties in a time-delay system. The WiNCS is evaluated with latency and throughput measurements in various environments. The experiment setup conforming to the IEEE 802.11 standard achieves an average latency of 1.3 ms and a data throughput of 3.000 kB/s up to a distance of 70 m. The results demonstrate the feasibility of real-time closed-loop control with the proposed concept.
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paper_title: Predicting mobile video inter-download times with Hidden Markov Models

paper_content:

Saving energy in mobile networks can be achieved by intelligently deactivating basestations. One way to know when to activate or deactivate basestations is understanding user behavior. In this paper, we look at the segment download behavior of users of mobile video streaming in adaptive bit-rate streaming systems with segmented downloads. From a large trace of HTTP requests we extract mobile video sessions and their inter-download times. We evaluate if Hidden Markov Models are feasible to model and predict when a user will request segments in the future. We further analyse how choosing model parameters influences the prediction quality.
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paper_title: Traffic prediction based resource configuration in space-based systems

paper_content:

In this paper, we considers the resource allocation problems for video transmission in space based information networks. The queueing system analyzed in this work is composed of multiple users and a single server. To minimize both of the time average cost and delay of the system, and subject to the constraint that the queues in the system must be stable, we introduce a predictive backpressure algorithm into the consideration of resource allocation to make decision on which packets to be served first. Meanwhile, a multi-resolution wavelet decomposition based backpropagation neural network for the prediction of video traffic is designed in this paper. Performances of the proposed video traffic prediction system and resource allocation scheme are analyzed in the simulations. Results indicate that the prediction accuracy for the video traffic is improved according to the proposed prediction system, and the delay of the queueing system can be reduced through this prediction based resource allocation.
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paper_title: MOST: Mobile Broadband Network Optimization Using Planned Spatio-Temporal Events

paper_content:

Continuous optimization of Mobile Broadband Networks (MBNs) is done to simultaneously optimize and balance some performance factors such as Quality of Service (QoS) and energy usage. Initial MBN optimization approaches used various heuristics, while more advanced approaches have investigated the MBN optimization potential using predictions of future node loads based on historical load data. However, using only historical load data for prediction often fails when events occur. This paper presents MOST (Mobile broadband network Optimization using planned Spatio-Temporal events) approach that additionally uses external spatio-temporal information about planned events, e.g., the time and place of big concerts or football matches, to improve the prediction quality, thus enabling more correct MBN optimization. A simulation using experimental data from a mobile broadband network in Denmark shows that MOST is able to improve the prediction quality very significantly, and thus enables significantly better MBN optimizations to be performed.
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paper_title: Tracking and Predicting End-to-End Quality in Wireless Community Networks

paper_content:

Community networks are an emergent model with mottos like "a free net for everyone is possible" or "don't buy the network, be the network". Their social impact is measurable, as the community is provided with the right and opportunity of communication. The combination of wired and wireless links in these networks, and the unreliable nature of the wireless medium, poses several challenges to the routing protocol. End-to End quality tracking helps the routing layer to select paths that maximize the delivery rate and minimize traffic congestion. We believe that End-to-End quality prediction can be a technique that surpasses End-to-End quality tracking by foreseeing which paths are more likely to change quality. In this work, we focus on End-to-End quality prediction by means of time-series analysis. We apply this prediction technique in the routing layer of large scale, distributed, and decentralized networks. We demonstrate that it is possible to accurately predict End-to-End Quality with an average Mean Absolute Error of just 2.4%. Particularly, we analyze the path properties and path ETX behavior to identify the best prediction algorithm. Moreover, we analyze the EtEQ prediction accuracy some steps ahead in the future and also its dependency of the time of the day.
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paper_title: Understanding traffic dynamics in cellular data networks

paper_content:

We conduct the first detailed measurement analysis of network resource usage and subscriber behavior using a large-scale data set collected inside a nationwide 3G cellular data network. The data set tracks close to a million subscribers over thousands of base stations. We analyze individual subscriber behaviors and observe a significant variation in network usage among subscribers. We characterize subscriber mobility and temporal activity patterns and identify their relation to traffic volume. We then investigate how efficiently radio resources are used by different subscribers as well as by different applications. We also analyze the network traffic from the point of view of the base stations and find significant temporal and spatial variations in different parts of the network, while the aggregated behavior appears predictable. Broadly, our observations deliver important insights into network-wide resource usage. We describe implications in pricing, protocol design and resource and spectrum management.
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paper_title: Resource Allocation With Video Traffic Prediction in Cloud-Based Space Systems

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This paper considers the resource allocation problems for video transmission in space-based information networks. The queueing system analyzed in this study is constituted by multiple users and a single server. The server is operated as a cloud that can sense the traffic arrivals to each user's queue and then allocates the transmission resource and service rate for users. The objectives are to make configurations over time to minimize the time average cost of the system, and to minimize the waiting time of packets after they enter the queue. Meanwhile, the constraints on the queue stability of the system must be satisfied. In this paper, we introduce a predictive backpressure algorithm, which considers the future arrivals with a certain prediction window size into the consideration of resource allocation to make decisions on which packets to be served first. In addition, this paper designs a multiresolution wavelet decomposition-based backpropagation network for the prediction of video traffic, which exhibits the long-range dependence property. Simulation results indicate that the delay of the queueing system can be reduced through this prediction-based resource allocation, and the prediction accuracy for the video traffic is improved according to the proposed prediction system.
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paper_title: Cloud Analytics for Wireless Metric Prediction - Framework and Performance

paper_content:

The knowledge of a future link quality for a user equipment (UE) in wireless networks may be used by the application and TCP layer so that user's experience is optimized. The metrics that are relevant are available at the core of the radio link and the application or TCP layers have no knowledge of the wireless metrics. With a signaling protocol to the application/TCP layer and a prediction mechanism of the future link quality the applications will be able to avoid buffer overflows and/or congestion. In this paper we identify metrics that are suitable for application/TCP control, and analyze the performance of the prediction of the metrics. We show that the prediction of wireless metrics can be made with low error (3-10% MAPE) even with high cloud latency. This is a significant result as it states that predictions in the cloud of even short term LTE metrics are possible and that the predictions are fairly accurate to influence the proper operation of applications/TCP in wireless environments.
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paper_title: Improving QoS in High-Speed Mobility Using Bandwidth Maps

paper_content:

It is widely evidenced that location has a significant influence on the actual bandwidth that can be expected from Wireless Wide Area Networks (WWANs), e.g., 3G. Because a fast-moving vehicle continuously changes its location, vehicular mobile computing is confronted with the possibility of significant variations in available network bandwidth. While it is difficult for providers to eliminate bandwidth disparity over a large service area, it may be possible to map network bandwidth to the road network through repeated measurements. In this paper, we report results of an extensive measurement campaign to demonstrate the viability of such bandwidth maps. We show how bandwidth maps can be interfaced with adaptive multimedia servers and the emerging vehicular communication systems that use on-board mobile routers to deliver Internet services to the passengers. Using simulation experiments driven by our measurement data, we quantify the improvement in Quality of Service (QoS) that can be achieved by taking advantage of the geographical knowledge of bandwidth provided by the bandwidth maps. We find that our approach reduces the frequency of disruptions in perceived QoS for both audio and video applications in high-speed vehicular mobility by several orders of magnitude.
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paper_title: CFA: a practical prediction system for video QoE optimization

paper_content:

Many prior efforts have suggested that Internet video Quality of Experience (QoE) could be dramatically improved by using data-driven prediction of video quality for different choices (e.g., CDN or bitrate) to make optimal decisions. However, building such a prediction system is challenging on two fronts. First, the relationships between video quality and observed session features can be quite complex. Second, video quality changes dynamically. Thus, we need a prediction model that is (a) expressive enough to capture these complex relationships and (b) capable of updating quality predictions in near real-time. Unfortunately, several seemingly natural solutions (e.g., simple machine learning approaches and simple network models) fail on one or more fronts. Thus, the potential benefits promised by these prior efforts remain unrealized. We address these challenges and present the design and implementation of Critical Feature Analytics (CFA). The design of CFA is driven by domain-specific insights that video quality is typically determined by a small subset of critical features whose criticality persists over several tens of minutes. This enables a scalable and accurate workflow where we automatically learn critical features for different sessions on coarse-grained timescales, while updating quality predictions in near real-time. Using a combination of a real-world pilot deployment and trace-driven analysis, we demonstrate that CFA leads to significant improvements in video quality; e.g., 32% less buffering time and 12% higher bitrate than a random decision maker.
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paper_title: When Backpressure Meets Predictive Scheduling

paper_content:

Motivated by the increasing popularity of learning and predicting human user behavior in communication and computing systems, in this paper, we investigate the fundamental benefit of predictive scheduling, i.e., predicting and pre-serving arrivals, in controlled queueing systems. Based on a lookahead-window prediction model, we first establish a novel queue-equivalence between the predictive queueing system with a fully efficient scheduling scheme and an equivalent queueing system without prediction. This result allows us to analytically demonstrate that predictive scheduling necessarily improves system delay performance and drives it to zero with increasing prediction power. It also enables us to exactly determine the required prediction power for different systems and study its impact on tail delay. We then propose the Predictive Backpressure (PBP) algorithm for achieving optimal utility performance in such predictive systems. PBP efficiently incorporates prediction into stochastic system control and avoids the great complication due to the exponential state space growth in the prediction window size. We show that PBP achieves a utility performance that is within $O(\epsilon)$ of the optimal, for any $\epsilon > 0$ , while guaranteeing that the system delay distribution is a shifted-to-the-left version of that under the original Backpressure algorithm. Hence, the average delay under PBP is strictly better than that under Backpressure, and vanishes with increasing prediction window size. This implies that the resulting utility-delay tradeoff with predictive scheduling can beat the known optimal $[O(\epsilon),O(\log(1/\epsilon))]$ tradeoff for systems without prediction. We also develop the Predictable-Only PBP (POPBP) algorithm and show that it effectively reduces packet delay in systems where traffic can only be predicted but not pre-served.
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paper_title: OSCAR: an optimized stall-cautious adaptive bitrate streaming algorithm for mobile networks

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The design of an adaptive video client for mobile users is challenged by the frequent changes in operating conditions. Such conditions present a seemingly insurmountable challenge to adaptation algorithms, which may fail to find a balance between video rate, stalls, and rate-switching. In an effort to achieve the ideal balance, we design OSCAR, a novel adaptive streaming algorithm whose adaptation decisions are optimized to avoid stalls while maintaining high video quality. Our performance evaluation, using real video and channel traces from both 3G and 4G networks, shows that OSCAR achieves the highest percentage of stall-free sessions while maintaining a high quality video in comparison to the state-of-the-art algorithms.
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paper_title: SQUAD: a spectrum-based quality adaptation for dynamic adaptive streaming over HTTP

paper_content:

The application-layer based control loops of dynamic adaptive streaming over HTTP (DASH) make video bitrate selection a complex problem. In this work, we review and present new insights into the challenges of DASH rate adaptation. We identify several critical issues that contribute to the degradation of DASH performance with respect to the rate control loops of DASH and TCP. We then introduce a novel DASH quality adaptation algorithm SQUAD, which is specifically designed to ensure high quality of experience (QoE). We implement and test our algorithm together with a number of state-of-the-art quality adaptation algorithms. Through extensive experiments on both testbed and cross-Atlantic Internet scenarios, we show that by sacrificing little to none in average quality bitrate, SQUAD provides significantly better QoE in terms of number and magnitude of quality switches.
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paper_title: Commute path bandwidth traces from 3G networks: analysis and applications

paper_content:

In this dataset paper, we present and make available real-world measurements of the throughput that was achieved at the application layer when adaptive HTTP streaming was performed over 3G networks using mobile devices. For the streaming sessions, we used popular commute routes in and around Oslo (Norway) traveling with different types of public transportation (metro, tram, train, bus and ferry). We also have a few logs using a car. Each log provides a times-tamp, GPS coordinates and the measured number of bytes downloaded for approximately every second of the route. The dataset can be used in several ways, but the most obvious application is to emulate the same network bandwidth behavior (on specific geographical positions) for repeated experiments.
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paper_title: Content Caching and Scheduling in Wireless Networks With Elastic and Inelastic Traffic

paper_content:

The rapid growth of wireless content access implies the need for content placement and scheduling at wireless base stations. We study a system under which users are divided into clusters based on their channel conditions, and their requests are represented by different queues at logical front ends. Requests might be elastic (implying no hard delay constraint) or inelastic (requiring that a delay target be met). Correspondingly, we have request queues that indicate the number of elastic requests, and deficit queues that indicate the deficit in inelastic service. Caches are of finite size and can be refreshed periodically from a media vault. We consider two cost models that correspond to inelastic requests for streaming stored content and real-time streaming of events, respectively. We design provably optimal policies that stabilize the request queues (hence ensuring finite delays) and reduce average deficit to zero [hence ensuring that the quality-of-service (QoS) target is met] at small cost. We illustrate our approach through simulations.
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paper_title: Predictive delay-aware network selection in data offloading

paper_content:

To address the increasingly severe congestion problem in cellular networks, mobile operators are actively considering offloading the cellular traffic to other complementary networks. In this paper, we study the online network selection problem in operator-initiated data offloading with multiple mobile users, taking into account the operation cost, queueing delay, and traffic load in different access networks (e.g., cellular macrocell, femtocell, and Wi-Fi networks). We first design a Delay-Aware Network Selection (DNS) algorithm based on the Lyapunov optimization technique. The DNS algorithm yields an operation cost within O (1/V) bound of the optimal value, and guarantees an O (V) traffic delay for any control parameter V > 0. Next, we incorporate the prediction of users' mobilities and traffic arrivals into the network selection. Specifically, we assume that the users' locations and traffic arrivals in the next few time slots can be estimated accurately, and propose a Predictive Delay-Aware Network Selection (P-DNS) algorithm to utilize this information based on a novel frame-based design. We characterize the performance bounds of P-DNS in terms of cost-delay tradeoff theoretically. To further reduce the computational complexity, we propose a Greedy Predictive Delay-Aware Network Selection (GP-DNS) algorithm, where the operator solves the network selection problem approximately and iteratively. Numerical results show that GP-DNS improves the cost-delay performance over DNS, and reduces the queueing delay by roughly 40% with the same operation cost.
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paper_title: Developing a predictive model of quality of experience for internet video

paper_content:

Improving users' quality of experience (QoE) is crucial for sustaining the advertisement and subscription based revenue models that enable the growth of Internet video. Despite the rich literature on video and QoE measurement, our understanding of Internet video QoE is limited because of the shift from traditional methods of measuring video quality (e.g., Peak Signal-to-Noise Ratio) and user experience (e.g., opinion scores). These have been replaced by new quality metrics (e.g., rate of buffering, bitrate) and new engagement centric measures of user experience (e.g., viewing time and number of visits). The goal of this paper is to develop a predictive model of Internet video QoE. To this end, we identify two key requirements for the QoE model: (1) it has to be tied in to observable user engagement and (2) it should be actionable to guide practical system design decisions. Achieving this goal is challenging because the quality metrics are interdependent, they have complex and counter-intuitive relationships to engagement measures, and there are many external factors that confound the relationship between quality and engagement (e.g., type of video, user connectivity). To address these challenges, we present a data-driven approach to model the metric interdependencies and their complex relationships to engagement, and propose a systematic framework to identify and account for the confounding factors. We show that a delivery infrastructure that uses our proposed model to choose CDN and bitrates can achieve more than 20\% improvement in overall user engagement compared to strawman approaches.
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paper_title: Matching theory for future wireless networks: fundamentals and applications

paper_content:

The emergence of novel wireless networking paradigms such as small cell and cognitive radio networks has forever transformed the way in which wireless systems are operated. In particular, the need for self-organizing solutions to manage the scarce spectral resources has become a prevalent theme in many emerging wireless systems. In this article, the first comprehensive tutorial on the use of matching theory, a Nobel Prize winning framework, for resource management in wireless networks is developed. To cater for the unique features of emerging wireless networks, a novel, wireless-oriented classification of matching theory is proposed. Then the key solution concepts and algorithmic implementations of this framework are exposed. The developed concepts are applied in three important wireless networking areas in order to demonstrate the usefulness of this analytical tool. Results show how matching theory can effectively improve the performance of resource allocation in all three applications discussed.
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paper_title: On Optimal Proactive Caching for Mobile Networks With Demand Uncertainties

paper_content:

Mobile data users are known to possess predictable characteristics both in their interests and activity patterns. Yet, their service is predominantly performed, especially at the wireless edges, “reactively” at the time of request, typically when the network is under heavy traffic load. This strategy incurs excessive costs to the service providers to sustain on-time (or delay-intolerant) delivery of data content, while their resources are left underutilized during the light-loaded hours. This motivates us in this work to study the problem of optimal “proactive” caching whereby, future delay-intolerant data demands can be served within a given prediction window ahead of their actual time-of-arrival to minimize service costs. To that end, we first establish fundamental bounds on the minimum possible cost achievable by any proactive policy, as a function of the prediction uncertainties. These bounds provide interesting insights on the impact of uncertainty on the maximum achievable proactive gains. We then propose specific proactive caching strategies, both for uniform and fluctuating demand patterns, that are asymptotically-optimal in the limit as the prediction window size grows while the prediction uncertainties remain fixed. We further establish the exponential convergence rate characteristics of our proposed solutions to the optimal, revealing close-to-optimal performance characteristics of our designs even with small prediction windows. Also, proactive design is contrasted with its reactive and delay-tolerant counter-parts to obtain interesting results on the unavoidable costs of uncertainty and the potentially remarkable gains of proactive operation.
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paper_title: Context-Aware Scheduling of Joint Millimeter Wave and Microwave Resources for Dual-Mode Base Stations

paper_content:

One of the most promising approaches to overcome the drastic channel variations of millimeter wave (mmW) communications is to deploy dual-mode base stations that integrate both mmW and microwave (pW) frequencies. Reaping the benefits of a dual-mode operation requires scheduling mechanisms that can allocate resources efficiently and jointly at both frequency bands. In this paper, a novel resource allocation framework is proposed that exploits users' context, in terms of user application (UA) delay requirements, to maximize the quality-of-service (QoS) of a dual-mode base station. In particular, such a context-aware approach enables the network to dynamically schedule UAs, instead of users, thus providing more precise delay guarantees and a more efficient exploitation of the mmW resources. The scheduling of UAs is formulated as a one-to-many matching problem between UAs and resources and a novel algorithm is proposed to solve it. The proposed algorithm is shown to converge to a two-sided stable matching between UAs and network resources. Simulation results show that the proposed approach outperforms classical CSI-based scheduling in terms of the per UA QoS, yielding up to 36% improvement. The results also show that exploiting mmW resources provides significant traffic offloads reaching up to 43% from μW band.
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paper_title: Exploiting mobility prediction for mobility & popularity caching and DASH adaptation

paper_content:

We present our recent work investigating how mobility prediction can be exploited for improving the performance of mobile users in two directions: proactive caching requested content close to the network attachment points where a mobile has a high probability to connect to and DASH (Dynamic Adaptive Streaming over HTTP) video quality adaptation. For proactive caching we discuss a new model to proactively cache content based on both mobility prediction and content popularity. An important feature of the model is that it dynamically adapts caching decisions to the relative importance of the two factors. For DASH adaptation we discuss a procedure that exploits mobility and throughput prediction to select the quality levels of video segments requested by a DASH player in order to achieve improved QoE, in terms of both high video quality and few video quality switches.
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paper_title: Joint Smart Pricing and Proactive Content Caching for Mobile Services

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In this work, we formulate and study the profit maximization problem for a wireless service provider (SP) that encounters time-varying, yet partially predictable, demand characteristics. The disparate demand levels throughout the course of the day yield excessive service cost in the peak hour that substantially hurts the reaped profit. With the SP's ability to track and statistically predict future requests of its users, we propose to enable proactive caching of the peak hour demand ahead during off-peak times. Thus, network traffic will be smoothed out, while end-users' activity patterns are undisturbed. In addition, the SP is able to assign personalized pricing policies that strike the best balance between enhancing the certainty about the future demand for optimal proactive caching and maximizing the revenue collected from end-users. Comparing the proposed system's performance to the baseline scenario of the existing practice of no-proactive service, we show that the SP attains profit gain that grows with number of users, at least, as the first derivative of the cost function. Moreover, end-users that receive proactive caching services make strictly positive savings. Thus, we essentially demonstrate the win-win situation to be reaped through the exploitation of the consistent users' activity.
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paper_title: Context-Aware Small Cell Networks: How Social Metrics Improve Wireless Resource Allocation

paper_content:

In this paper, a novel approach for optimizing and managing resource allocation in wireless small cell networks (SCNs) with device-to-device (D2D) communication is proposed. The proposed approach allows to jointly exploit both the wireless and social context of wireless users for optimizing the overall allocation of resources and improving traffic offload in SCNs. This context-aware resource allocation problem is formulated as a matching game in which user equipments (UEs) and resource blocks (RBs) rank one another, based on utility functions that capture both wireless and social metrics. Due to social interrelations, this game is shown to belong to a class of matching games with peer effects. To solve this game, a novel, selforganizing algorithm is proposed, using which UEs and RBs can interact to decide on their desired allocation. The proposed algorithm is then proven to converge to a two-sided stable matching between UEs and RBs. The properties of the resulting stable outcome are then studied and assessed. Simulation results using real social data show that clustering of socially connected users allows to offload a substantially larger amount of traffic than the conventional context-unaware approach. These results show that exploiting social context has high practical relevance in saving resources on the wireless links and on the backhaul.
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paper_title: Behavior-based mobility prediction for seamless handoffs in mobile wireless networks

paper_content:

The field of wireless networking has received unprecedented attention from the research community during the last decade due to its great potential to create new horizons for communicating beyond the Internet. Wireless LANs (WLANs) based on the IEEE 802.11 standard have become prevalent in public as well as residential areas, and their importance as an enabling technology will continue to grow for future pervasive computing applications. However, as their scale and complexity continue to grow, reducing handoff latency is particularly important. This paper presents the Behavior-based Mobility Prediction scheme to eliminate the scanning overhead incurred in IEEE 802.11 networks. This is achieved by considering not only location information but also group, time-of-day, and duration characteristics of mobile users. This captures short-term and periodic behavior of mobile users to provide accurate next-cell predictions. Our simulation study of a campus network and a municipal wireless network shows that the proposed method improves the next-cell prediction accuracy by 23~43% compared to location-only based schemes and reduces the average handoff delay down to 24~25 ms.
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paper_title: Proactive small cell networks

paper_content:

Proactive scheduling in mobile networks is known as a way of using network resources efficiently. In this work, we investigate proactive Small Cell Networks (SCNs) from a caching perspective. We first assume that these small base stations are deployed with high capacity storage units but have limited capacity backhaul links. We then describe the model and define a Quality of Experience (QoE) metric in order to satisfy a given file request. The optimization problem is formulated in order to maximize this QoE metric for all requests under the capacity constraints. We solve this problem by introducing an algorithm, called PropCaching (proactive popularity caching), which relies on the popularity statistics of the requested files. Since not all requested files can be cached due to storage constraints, the algorithm selects the files with the highest popularities until the total storage capacity is achieved. Consecutively, the proposed caching algorithm is compared with random caching. Given caching and sufficient capacity of the wireless links, numerical results illustrate that the number of satisfied requests increases. Moreover, we show that PropCaching performs better than random caching in most cases. For example, for R = 192 number of requests and a storage ratio γ = 0.25 (storage capacity over sum of length of all requested files), the satisfaction in PropCaching is 85% higher than random caching and the backhaul usage is reduced by 10%.
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paper_title: Social Network Aware Device-to-Device Communication in Wireless Networks

paper_content:

Device-to-device (D2D) communication is seen as a major technology to overcome the imminent wireless capacity crunch and to enable new application services. In this paper, a novel social-aware approach for optimizing D2D communication by exploiting two layers, namely the social network layer and the physical wireless network layer, is proposed. In particular, the physical layer D2D network is captured via the users' encounter histories. Subsequently, an approach, based on the so-called Indian Buffet Process, is proposed to model the distribution of contents in the users' online social networks. Given the social relations collected by the base station, a new algorithm for optimizing the traffic offloading process in D2D communications is developed. In addition, the Chernoff bound and approximated cumulative distribution function (cdf) of the offloaded traffic are derived and the validity of the bound and cdf is proven. Simulation results based on real traces demonstrate the effectiveness of our model and show that the proposed approach can offload the network's traffic successfully.
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paper_title: A context-aware matching game for user association in wireless small cell networks

paper_content:

Small cell networks are seen as a promising technology for boosting the performance of future wireless networks. In this paper, we propose a novel context-aware user-cell association approach for small cell networks that exploits the information about the velocity and trajectory of the users while also taking into account their quality of service (QoS) requirements. We formulate the problem in the framework of matching theory with externalities in which the agents, namely users and small cell base stations (SCBSs), have strict interdependent preferences over the members of the opposite set. To solve the problem, we propose a novel algorithm that leads to a stable matching among the users and SCBSs. We show that the proposed approach can better balance the traffic among the cells while also satisfying the QoS of the users. Simulation results show that the proposed matching algorithm yields significant performance advantages relative to traditional context-unaware approaches.
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paper_title: Human mobility prediction based on individual and collective geographical preferences

paper_content:

Understanding and predicting human mobility is a crucial component of transportation planning and management. In this paper we propose a new model to predict the location of a person over time based on individual and collective behaviors. The model is based on the person's past trajectory and the geographical features of the area where the collectivity moves, both in terms of land use, points of interests and distance of trips. The effectiveness of the proposed prediction model is tested using a massive mobile phone location dataset available for the Boston metropolitan area. Experimental results show good levels of accuracy in terms of prediction error and prove the advantage of using the collective behavior in the prediction model.
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paper_title: Mining User Mobility Features for Next Place Prediction in Location-Based Services

paper_content:

Mobile location-based services are thriving, providing an unprecedented opportunity to collect fine grained spatio-temporal data about the places users visit. This multi-dimensional source of data offers new possibilities to tackle established research problems on human mobility, but it also opens avenues for the development of novel mobile applications and services. In this work we study the problem of predicting the next venue a mobile user will visit, by exploring the predictive power offered by different facets of user behavior. We first analyze about 35 million check-ins made by about 1 million Foursquare users in over 5 million venues across the globe, spanning a period of five months. We then propose a set of features that aim to capture the factors that may drive users' movements. Our features exploit information on transitions between types of places, mobility flows between venues, and spatio-temporal characteristics of user check-in patterns. We further extend our study combining all individual features in two supervised learning models, based on linear regression and M5 model trees, resulting in a higher overall prediction accuracy. We find that the supervised methodology based on the combination of multiple features offers the highest levels of prediction accuracy: M5 model trees are able to rank in the top fifty venues one in two user check-ins, amongst thousands of candidate items in the prediction list.
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paper_title: Many-to-many matching games for proactive social-caching in wireless small cell networks

paper_content:

In this paper, we address the caching problem in small cell networks from a game theoretic point of view. In particular, we formulate the caching problem as a many-to-many matching game between small base stations and service providers' servers. The servers store a set of videos and aim to cache these videos at the small base stations in order to reduce the experienced delay by the end-users. On the other hand, small base stations cache the videos according to their local popularity, so as to reduce the load on the backhaul links. We propose a new matching algorithm for the many-to-many problem and prove that it reaches a pairwise stable outcome. Simulation results show that the number of satisfied requests by the small base stations in the proposed caching algorithm can reach up to three times the satisfaction of a random caching policy. Moreover, the expected download time of all the videos can be reduced significantly.
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paper_title: Anticipatory Caching in Small Cell Networks: A Transfer Learning Approach

paper_content:

Locally caching contents at the network edge constitutes one of the five most disruptive paradigms in 5G networks [1]. Recent results have shown that dynamic caching can significantly offload different parts of the network including the Radio access networks (RANs) and core network (CN), by smartly prefetching and storing contents closer to the end-users. In parallel to that, the era of pushing contents through the network on a best-effort basis ignoring who end-users are and what they are doing with their devices has dawned, calling for a truly context-aware and proactive networking paradigm [2]. As a result, edge caching has taken the recent 5G literature by storm over the last few years as evidenced in [2]–[6]. In [6], the concept of femtocaching was proposed addressing the problem of capacity-limited backhaul links by embedding base stations with high-storage units. In [2], a novel edge-centric networking paradigm was proposed in which network nodes (i.e., base station (BS) and/or user terminals (UTs)) proactively cache judiciously selected contents at the network edge. Exploiting both spatial and social caching coupled with suitable device-to-device (D2D) communication was shown to efficiently offload the backhaul traffic and enhancing the overall network performance in terms of cache-hit-rates and users' satisfaction ratios. Therein, a proactive caching proce-dure is formulated as a supervised machine learning problem and collaborative filtering (CF) techniques are used to estimate the file popularity matrix exploiting users-files correlations. Notwithstanding this fact, the file popularity matrix remains typically large and sparse in practice, rendering CF techniques sub-optimal suffering from data sparseness and cold-start problem, which are major challenges in the machine learning community [7]. In this work, we build upon the work in [2] and propose a more efficient machine learning technique, using the frame-work of transfer learning (TL) [7]. Indeed, in many real-world applications, it is expensive or even impossible to collect and label training data to build suitable prediction models. With this in mind, TL is seen as a suitable framework which allows to exploit data from other rich information sources (referred to as source domain) to further improve the prediction task in the target domain. TL has been traditionally used in data mining This research has been supported by the ERC Starting Grant 305123 MORE (Advanced Mathematical Tools for Complex Network Engineering), the SHARING project under the Finland grant 128010 and the project BESTCOM. problems such as classification, regression and very recently in the context of CF [7]. 1) Transfer Learning: In a nutshell, TL can be classified into inductive, transductive and unsupervised transfer learning depending on the availability of the source and target domain labels. Research issues in transfer learning boil down to: 1) what to transfer, 2) how to transfer, and 3) when to transfer. While "what to transfer" studies which part of the knowledge can be transferred across domains or tasks, "when to transfer" deals with the issue of knowing when is best to transfer the knowledge to avoid negative transfer, notably when the source and target domains become unrelated. Finally, "how to transfer" deals with knowledge extraction which needs to be transferred. A comprehensive survey of transfer learning is found in [7]. 2) Contribution: The basic idea of the proposed learning approach is to alleviate the data sparsity problem encountered in most CF problems, by learning and transferring the rich contextual information (i.e., source domain), to better estimate the (large-scale) file popularity matrix in the target domain. We assume that the knowledge extracted from the source domain stems from the interaction of users accessing/sharing and recommending files within their social community via D2D (Web2.0-like). Instead of learning from scratch in the target domain, the nice feature of the TL approach lies in judiciously extracting collaborative social behavior information from the source domain to aid in the learning in the target domain, which will be explained in the sequel. To the best of our knowledge this is perhaps the first contribution of transfer learning in RANs. A. Network Model
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paper_title: Living on the edge: The role of proactive caching in 5G wireless networks

paper_content:

This article explores one of the key enablers of beyond 4G wireless networks leveraging small cell network deployments, proactive caching. Endowed with predictive capabilities and harnessing recent developments in storage, context awareness, and social networks, peak traffic demands can be substantially reduced by proactively serving predictable user demands via caching at base stations and users' devices. In order to show the effectiveness of proactive caching, we examine two case studies that exploit the spatial and social structure of the network, where proactive caching plays a crucial role. First, in order to alleviate backhaul congestion, we propose a mechanism whereby files are proactively cached during off-peak periods based on file popularity and correlations among user and file patterns. Second, leveraging social networks and D2D communications, we propose a procedure that exploits the social structure of the network by predicting the set of influential users to (proactively) cache strategic contents and disseminate them to their social ties via D2D communications. Exploiting this proactive caching paradigm, numerical results show that important gains can be obtained for each case study, with backhaul savings and a higher ratio of satisfied users of up to 22 and 26 percent, respectively. Higher gains can be further obtained by increasing the storage capability at the network edge.
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paper_title: FemtoCaching: Wireless video content delivery through distributed caching helpers

paper_content:

We suggest a novel approach to handle the ongoing explosive increase in the demand for video content in wireless/mobile devices. We envision femtocell-like base stations, which we call helpers, with weak backhaul links but large storage capacity. These helpers form a wireless distributed caching network that assists the macro base station by handling requests of popular files that have been cached. Due to the short distances between helpers and requesting devices, the transmission of cached files can be done very efficiently.
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paper_title: Context-Aware Resource Allocation to Improve the Quality of Service of Heterogeneous Traffic

paper_content:

By supporting advanced multimedia applications and multitasking, recent Smartphone operation systems offer great flexibility to the users but make these devices even more difficult to serve. Now, operators of cellular networks face a situation where many applications and services generate highly heterogeneous and bursty traffic patterns. To cope with this challenge, we introduce Context-Aware Resource Allocation (CARA). This new approach enables a scheduler to exploit new information from the users' environment, thereby, allocating its resources more efficiently. We demonstrate CARA's high performance and feasibility by studying it as network utility maximization problem. Comparing our results to traditional proportional fair scheduling clearly shows that knowing more about a user's context is worth the effort.
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paper_title: Context-aware resource allocation for cellular wireless networks

paper_content:

Current cellular networks are often overloaded by Smartphone traffic, while the users’ Quality of Service (QoS) demands are not met. To cope with this problem, we demonstrate a new radio resource management approach. With Context-Aware Resource Allocation, the base station’s scheduler (i) observes Context Information (CI) from the user’s environment and (ii) utilizes this knowledge for an efficient throughput-delay tradeoff. After introducing our framework for accessing CI from the handheld’s applications and operating system, we use time-utility functions to develop a practical scheduling algorithm. Studying this heuristic under various traffic assumptions shows that our context-aware scheduler can support three times the load of proportional fair scheduling, at equal capacity and utility. Thus, even a small degree of CI increases the wireless links’ efficiency without sacrificing the users’ QoS.
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paper_title: The impact of social distance on utility based resource allocation in next generation networks

paper_content:

Social networks have rapidly grown over the last years. The socio-technical information that they include may be exploited to improve the performance of the underlying communication network. This is of utmost importance especially in Next Generation Networks (NGNs), which provide multiple services with increased bandwidth requirements. In this paper, the problem of resource allocation in a bottleneck link of a NGN is examined. To this end, a utility based technique is proposed, which integrates the information of an overlaid social network expressed by the social distance parameter. In this case, the utility function employed is reformed, so as to incorporate this parameter, which in this study is determined by the users' average popularity. Network users are classified into friendship classes according to the social distance parameter. Then, the users' resource allocation objective is formulated as an optimization problem with inequality constraints. Due to the increased problem's complexity, a numerical method is employed to estimate the optimal resource allocation. Numerical results are presented, which indicate the impact of the social distance parameter on resource allocation.
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paper_title: Spatial traffic prediction for wireless cellular system based on base stations social network

paper_content:

Understanding the spatial traffic patterns of wireless cellular system could facilitate the performance analysis and system design. In this paper, a novel method based on base stations social network (BSSN) is proposed for the prediction of spatial traffic in wireless cellular system. Firstly a BSSN, which can describe the spatial traffic relationship between base stations (BSs), is established with the real spatial-temporal cellular system traffic data. Then, the very important base stations (VIBS) can be selected based on BSSN from complex networks perspective. Finally, with the acquired traffic data of these VIBS, all the other BSs traffic can be predicted with the help of Support Vector Regression (SVR). The analytical results show that our proposed prediction method can effective predict the characteristics of traffics of the entire wireless cellular system; and by applying this method, only the traffic of the 8% of the entire BSs are required for an effective prediction with a mean error ratio less than 20%.
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paper_title: Dynamic prediction of traffic volume through Kalman filtering theory

paper_content:

Two models employing Kalman filtering theory are proposed for predicting short-term traffic volume. Prediction parameters are improved using the most recent prediction error and better volume prediction on a link is achieved by taking into account data from a number of links. Based on data collected from a street network in Nagoya City, average prediction error is found to be less than 9% and maximum error less than 30%. The new models perform substantially (up to 80%) better than UTCS-2.
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paper_title: Multi-scale high-speed network traffic prediction using k-factor Gegenbauer ARMA model

paper_content:

Gegenbauer autoregressive moving average (GARMA) model has the ability to capture both the short- and long-range dependent characteristics of the underlying data. GARMA has been used for modeling and forecasting of the financial time series that exhibits a long-range dependency (LRD). Since the high-speed network traffic exhibits a high degree of LRD characteristic, GARMA could be used for its modeling and prediction. In this paper, we present a simplified parameter estimation procedure and an adaptive prediction scheme for the k-factor GARMA model. The adaptation gives the model the ability to capture, the non-stationary characteristic of the data. The k-factor GARMA is applied to model four different types of real traffic data: MPEG and JPEG video, Ethernet and Internet. These models are then used to predict one-step-ahead traffic value at different timescales. The results show that the estimated parameters of the k-factor GARMA model provide a detailed and accurate presentation for the traffic characteristics in both time and frequency domain. We also demonstrate that the prediction performance of the k-factor GARMA model outperforms that of the traditional autoregressive (AR) model.
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paper_title: Long-term forecasting of Internet backbone traffic: observations and initial models

paper_content:

We introduce a methodology to predict when and where link additions/upgrades have to take place in an IP backbone network. Using SNMP statistics, collected continuously since 1999, we compute aggregate demand between any two adjacent PoPs and look at its evolution at time scales larger than one hour. We show that IP backbone traffic exhibits visible long term trends, strong periodicities, and variability at multiple time scales. Our methodology relies on the wavelet multiresolution analysis and linear time series models. Using wavelet multiresolution analysis, we smooth the collected measurements until we identify the overall long-term trend. The fluctuations around the obtained trend are further analyzed at multiple time scales. We show that the largest amount of variability in the original signal is due to its fluctuations at the 12 hour time scale. We model inter-PoP aggregate demand as a multiple linear regression model, consisting of the two identified components. We show that this model accounts for 98% of the total energy in the original signal, while explaining 90% of its variance. Weekly approximations of those components can be accurately modeled with low-order autoregressive integrated moving average (ARIMA) models. We show that forecasting the long term trend and the fluctuations of the traffic at the 12 hour time scale yields accurate estimates for at least six months in the future.
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paper_title: Behavior-based mobility prediction for seamless handoffs in mobile wireless networks

paper_content:

The field of wireless networking has received unprecedented attention from the research community during the last decade due to its great potential to create new horizons for communicating beyond the Internet. Wireless LANs (WLANs) based on the IEEE 802.11 standard have become prevalent in public as well as residential areas, and their importance as an enabling technology will continue to grow for future pervasive computing applications. However, as their scale and complexity continue to grow, reducing handoff latency is particularly important. This paper presents the Behavior-based Mobility Prediction scheme to eliminate the scanning overhead incurred in IEEE 802.11 networks. This is achieved by considering not only location information but also group, time-of-day, and duration characteristics of mobile users. This captures short-term and periodic behavior of mobile users to provide accurate next-cell predictions. Our simulation study of a campus network and a municipal wireless network shows that the proposed method improves the next-cell prediction accuracy by 23~43% compared to location-only based schemes and reduces the average handoff delay down to 24~25 ms.
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paper_title: Broadcasting with Prediction and Selective Forwarding in Vehicular Networks

paper_content:

Broadcasting in vehicular networks has attracted great interest in research community and industry. Broadcasting on disseminating information to individual vehicle beyond the transmission range is based on inter-vehicle communication systems. It is crucial to broadcast messages to other vehicles as fast as possible because the messages in vehicle communication systems are often emergency messages such as accident warning or alarm. In many current approaches, the message initiator or sender selects the node among its neighbors that is farthest away from it in the broadcasting direction and then assigns the node to rebroadcast the message once the node gets out of its range or after a particular time slot. However, this approach may select a nonoptimal candidate because it does not consider the moving status of vehicles including their moving directions and speeds. In this paper, we develop a new approach based on prediction of future velocity and selective forwarding. The current message sender selects the best candidate that will rebroadcast the message to other vehicles as fast as possible. Key to the decision making is to consider the candidates' previous moving status and predict the future moving trends of the candidates so that the message is spread out faster. In addition, this approach generates very low overhead. Simulations demonstrate that our approach significantly decreases end-to-end delay and improves message delivery ratio.
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paper_title: Structural Time Series Models and the Kalman Filter

paper_content:

The invention provides a fastening device, for the "uppers" of articles of footwear, having an elongated bridge member, a pair of cranks journalled on the bridge member at respective points along its length for pivotal movement in the direction of elongation of the bridge member, and means rotatably mounted on each crank at a spacing from its journal for coupling the crank to a respective one of two portions of the upper.
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paper_title: Real-time mobility tracking algorithms for cellular networks based on Kalman filtering

paper_content:

We propose two algorithms for real-time tracking of the location and dynamic motion of a mobile station in a cellular network using the pilot signal strengths from neighboring base stations. The underlying mobility model is based on a dynamic linear system driven by a discrete command process that determines the mobile station's acceleration. The command process is modeled as a semi-Markov process over a finite set of acceleration levels. The first algorithm consists of an averaging filter for processing pilot signal, strength measurements and two Kalman filters, one to estimate the discrete command process and the other to estimate the mobility state. The second algorithm employs a single Kalman filter without prefiltering and is able to track a mobile station even when a limited set of pilot signal measurements is available. Both of the proposed tracking algorithms can be used to predict future mobility behavior, which can be, useful in resource allocation applications. Our numerical results show that the proposed tracking algorithms perform accurately over a wide range of mobility parameter values.
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paper_title: Tracking and prediction of mobility without physical distance measurements in sensor networks

paper_content:

Existing methods for detection and tracking of mobile nodes and objects via sensor networks depend on physical localization of sensor nodes. Consequently, they suffer from disadvantages associated with localization, which is often costly, error prone, and in some cases even infeasible. Tracking mobile nodes in a topological coordinate domain is proposed as an alternative to that in geographic coordinate domain. Topological coordinates (TCs), the basis of recent developments in Topology Preserving Maps (TPMs), are derived from hop distances from each node to a small subset of nodes. A technique is presented to reduce the distortion closer to the edges of principal component based TPMs, thereby enhancing its accuracy, which in turn facilitates accurate mobility tracking. Topological Coordinate based Tracking and Prediction (TCTP) algorithm is proposed for tracking and predicting the position of mobile nodes in the TC domain. Simulation results presented show that tracking and detecting performance of TCTP is competitive with geographic coordinate based algorithms despite the fact that it relies only on hop distances to a subset of nodes.
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paper_title: PROTEUS: network performance forecast for real-time, interactive mobile applications

paper_content:

Real-time communication (RTC) applications such as VoIP, video conferencing, and online gaming are flourishing. To adapt and deliver good performance, these applications require accurate estimations of short-term network performance metrics, e.g., loss rate, one-way delay, and throughput. However, the wide variation in mobile cellular network performance makes running RTC applications on these networks problematic. To address this issue, various performance adaptation techniques have been proposed, but one common problem of such techniques is that they only adjust application behavior reactively after performance degradation is visible. Thus, proactive adaptation based on accurate short-term, fine-grained network performance prediction can be a preferred alternative that benefits RTC applications. In this study, we show that forecasting the short-term performance in cellular networks is possible in part due to the channel estimation scheme on the device and the radio resource scheduling algorithm at the base station. We develop a system interface called PROTEUS, which passively collects current network performance, such as throughput, loss, and one-way delay, and then uses regression trees to forecast future network performance. PROTEUS successfully predicts the occurrence of packet loss within a 0.5s time window for 98% of the time windows and the occurrence of long one-way delay for 97% of the time windows. We also demonstrate how PROTEUS can be integrated with RTC applications to significantly improve the perceptual quality. In particular, we increase the peak signal-to-noise ratio of a video conferencing application by up to 15dB and reduce the perceptual delay in a gaming application by up to 4s.
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paper_title: Characterizing and modeling internet traffic dynamics of cellular devices

paper_content:

Understanding Internet traffic dynamics in large cellular networks is important for network design, troubleshooting, performance evaluation, and optimization. In this paper, we present the results from our study, which is based upon a week-long aggregated flow level mobile device traffic data collected from a major cellular operator's core network. In this study, we measure and characterize the spatial and temporal dynamics of mobile Internet traffic. We distinguish our study from other related work by conducting the measurement at a larger scale and exploring mobile data traffic patterns along two new dimensions -- device types and applications that generate such traffic patterns. Based on the findings of our measurement analysis, we propose a Zipf-like model to capture the volume distribution of application traffic and a Markov model to capture the volume dynamics of aggregate Internet traffic. We further customize our models for different device types using an unsupervised clustering algorithm to improve prediction accuracy.
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paper_title: MOST: Mobile Broadband Network Optimization Using Planned Spatio-Temporal Events

paper_content:

Continuous optimization of Mobile Broadband Networks (MBNs) is done to simultaneously optimize and balance some performance factors such as Quality of Service (QoS) and energy usage. Initial MBN optimization approaches used various heuristics, while more advanced approaches have investigated the MBN optimization potential using predictions of future node loads based on historical load data. However, using only historical load data for prediction often fails when events occur. This paper presents MOST (Mobile broadband network Optimization using planned Spatio-Temporal events) approach that additionally uses external spatio-temporal information about planned events, e.g., the time and place of big concerts or football matches, to improve the prediction quality, thus enabling more correct MBN optimization. A simulation using experimental data from a mobile broadband network in Denmark shows that MOST is able to improve the prediction quality very significantly, and thus enables significantly better MBN optimizations to be performed.
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paper_title: Route Prediction from Trip Observations

paper_content:

This paper develops and tests algorithms for predicting the end-to-end route of a vehicle based on GPS observations of the vehicle’s past trips. We show that a large portion of a typical driver’s trips are repeated. Our algorithms exploit this fact for prediction by matching the first part of a driver’s current trip with one of the set of previously observed trips. Rather than predicting upcoming road segments, our focus is on making long term predictions of the route. We evaluate our algorithms using a large corpus of real world GPS driving data acquired from observing over 250 drivers for an average of 15.1 days per subject. Our results show how often and how accurately we can predict a driver’s route as a function of the distance already driven.
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paper_title: Taming Wireless Fluctuations by Predictive Queuing Using a Sparse-Coding Link-State Model

paper_content:

We introduce State-Informed Link-Layer Queuing (SILQ), a system that models, predicts, and avoids packet delivery failures caused by temporary wireless outages in everyday scenarios. By stabilizing connections in adverse link conditions, SILQ boosts throughput and reduces performance variation for network applications, for example by preventing unnecessary TCP timeouts due to dead zones, elevators, and subway tunnels. SILQ makes predictions in real-time by actively probing links, matching measurements to an overcomplete dictionary of patterns learned offline, and classifying the resulting sparse feature vectors to identify those that precede outages. We use a clustering method called sparse coding to build our data-driven link model, and show that it produces more variation-tolerant predictions than traditional loss-rate, location-based, or Markov chain techniques. We present extensive data collection and field-validation of SILQ in airborne, indoor, and urban scenarios of practical interest. We show how offline unsupervised learning discovers link-state patterns that are stable across diverse networks and signal-propagation environments. Using these canonical primitives, we train outage predictors for 802.11 (Wi-Fi) and 3G cellular networks to demonstrate TCP throughput gains of 4x with off-the-shelf mobile devices. SILQ addresses delivery failures solely at the link layer, requires no new hardware, and upholds the end-to-end design principle, to enable easy integration across applications, devices, and networks.
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paper_title: A Comparative Study of Collaborative Filtering Algorithms

paper_content:

Collaborative ltering is a rapidly advancing research area. Every year several new techniques are proposed and yet it is not clear which of the techniques work best and under what conditions. In this paper we conduct a study comparing several collaborative ltering techniques { both classic and recent state-of-the-art { in a variety of experimental contexts. Specically, we report conclusions controlling for number of items, number of users, sparsity level, performance criteria, and computational complexity. Our conclusions identify what algorithms work well and in what conditions, and contribute to both industrial deployment collaborative ltering algorithms and to the research community.
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paper_title: Survey of clustering algorithms

paper_content:

Data analysis plays an indispensable role for understanding various phenomena. Cluster analysis, primitive exploration with little or no prior knowledge, consists of research developed across a wide variety of communities. The diversity, on one hand, equips us with many tools. On the other hand, the profusion of options causes confusion. We survey clustering algorithms for data sets appearing in statistics, computer science, and machine learning, and illustrate their applications in some benchmark data sets, the traveling salesman problem, and bioinformatics, a new field attracting intensive efforts. Several tightly related topics, proximity measure, and cluster validation, are also discussed.
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paper_title: Predictive Caching Framework for Mobile Wireless Networks

paper_content:

With increasing popularity of Netflix, Yahoo! Video, etc., interactive multimedia services such as video-on-demand (VoD) provide an interesting and rich field of research. The advent of smarter wireless devices has surged the need for such services through wireless connectivity. However, personalization of individual user needs, reducing latency, coupled with maintaining low operational costs provides a challenging problem. In this paper, we propose an efficient VoD system, for wireless mobile devices, based on a novel caching algorithm, Intelligent Network Caching Algorithm (INCA) using analytics-driven look ahead scheme for both prefetch and replacement policies to deliver higher performance. This enables enhanced Quality Of Experience (QoE) of users with limited infrastructural changes and low operational cost. Alongside, we develop theoretical formulation of the QoE optimization problem that lies at the intersection of MPC (Markov Predictive Control) and MDP (Markov Decision Process). Empirical analysis over realistic user video query logs demonstrate better cache hit rate and QoE with low prefetch bandwidth, compared to existing caching schemes.
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paper_title: Mining User Mobility Features for Next Place Prediction in Location-Based Services

paper_content:

Mobile location-based services are thriving, providing an unprecedented opportunity to collect fine grained spatio-temporal data about the places users visit. This multi-dimensional source of data offers new possibilities to tackle established research problems on human mobility, but it also opens avenues for the development of novel mobile applications and services. In this work we study the problem of predicting the next venue a mobile user will visit, by exploring the predictive power offered by different facets of user behavior. We first analyze about 35 million check-ins made by about 1 million Foursquare users in over 5 million venues across the globe, spanning a period of five months. We then propose a set of features that aim to capture the factors that may drive users' movements. Our features exploit information on transitions between types of places, mobility flows between venues, and spatio-temporal characteristics of user check-in patterns. We further extend our study combining all individual features in two supervised learning models, based on linear regression and M5 model trees, resulting in a higher overall prediction accuracy. We find that the supervised methodology based on the combination of multiple features offers the highest levels of prediction accuracy: M5 model trees are able to rank in the top fifty venues one in two user check-ins, amongst thousands of candidate items in the prediction list.
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paper_title: WhereNext: a location predictor on trajectory pattern mining

paper_content:

The pervasiveness of mobile devices and location based services is leading to an increasing volume of mobility data.This side eect provides the opportunity for innovative methods that analyse the behaviors of movements. In this paper we propose WhereNext, which is a method aimed at predicting with a certain level of accuracy the next location of a moving object. The prediction uses previously extracted movement patterns named Trajectory Patterns, which are a concise representation of behaviors of moving objects as sequences of regions frequently visited with a typical travel time. A decision tree, named T-pattern Tree, is built and evaluated with a formal training and test process. The tree is learned from the Trajectory Patterns that hold a certain area and it may be used as a predictor of the next location of a new trajectory finding the best matching path in the tree. Three dierent best matching methods to classify a new moving object are proposed and their impact on the quality of prediction is studied extensively. Using Trajectory Patterns as predictive rules has the following implications: (I) the learning depends on the movement of all available objects in a certain area instead of on the individual history of an object; (II) the prediction tree intrinsically contains the spatio-temporal properties that have emerged from the data and this allows us to define matching methods that striclty depend on the properties of such movements. In addition, we propose a set of other measures, that evaluate a priori the predictive power of a set of Trajectory Patterns. This measures were tuned on a real life case study. Finally, an exhaustive set of experiments and results on the real dataset are presented.
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paper_title: A survey of techniques for internet traffic classification using machine learning

paper_content:

The research community has begun looking for IP traffic classification techniques that do not rely on `well known? TCP or UDP port numbers, or interpreting the contents of packet payloads. New work is emerging on the use of statistical traffic characteristics to assist in the identification and classification process. This survey paper looks at emerging research into the application of Machine Learning (ML) techniques to IP traffic classification - an inter-disciplinary blend of IP networking and data mining techniques. We provide context and motivation for the application of ML techniques to IP traffic classification, and review 18 significant works that cover the dominant period from 2004 to early 2007. These works are categorized and reviewed according to their choice of ML strategies and primary contributions to the literature. We also discuss a number of key requirements for the employment of ML-based traffic classifiers in operational IP networks, and qualitatively critique the extent to which the reviewed works meet these requirements. Open issues and challenges in the field are also discussed.
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paper_title: Developing a predictive model of quality of experience for internet video

paper_content:

Improving users' quality of experience (QoE) is crucial for sustaining the advertisement and subscription based revenue models that enable the growth of Internet video. Despite the rich literature on video and QoE measurement, our understanding of Internet video QoE is limited because of the shift from traditional methods of measuring video quality (e.g., Peak Signal-to-Noise Ratio) and user experience (e.g., opinion scores). These have been replaced by new quality metrics (e.g., rate of buffering, bitrate) and new engagement centric measures of user experience (e.g., viewing time and number of visits). The goal of this paper is to develop a predictive model of Internet video QoE. To this end, we identify two key requirements for the QoE model: (1) it has to be tied in to observable user engagement and (2) it should be actionable to guide practical system design decisions. Achieving this goal is challenging because the quality metrics are interdependent, they have complex and counter-intuitive relationships to engagement measures, and there are many external factors that confound the relationship between quality and engagement (e.g., type of video, user connectivity). To address these challenges, we present a data-driven approach to model the metric interdependencies and their complex relationships to engagement, and propose a systematic framework to identify and account for the confounding factors. We show that a delivery infrastructure that uses our proposed model to choose CDN and bitrates can achieve more than 20\% improvement in overall user engagement compared to strawman approaches.
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paper_title: Some tools for functional data analysis

paper_content:

Multivariate data analysis permits the study of observations which are finite sets of numbers, but modern data collection situations can involve data, or the processes giving rise to them, which are functions. Functional data analysis involves infinite dimensional processes and/or data. The paper shows how the theory of L-splines can support generalizations of linear modelling and principal components analysis to samples drawn from random functions. Spline smoothing rests on a partition of a function space into two orthogonal subspaces, one of which contains the obvious or structural components of variation among a set of observed functions, and the other of which contains residual components. This partitioning is achieved through the use of a linear differential operator, and we show how the theory of polynomial splines can be applied more generally with an arbitrary operator and associated boundary constraints. These data analysis tools are illustrated by a study of variation in temperature-precipitation patterns among some Canadian weather-stations.
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paper_title: Predicting a user's next cell with supervised learning based on channel states

paper_content:

Knowing a user's next cell allows more efficient resource allocation and enables new location-aware services. To anticipate the cell a user will hand-over to, we introduce a new machine learning based prediction system. Therein, we formulate the prediction as a classification problem based on information that is readily available in cellular networks. Using only Channel State Information (CSI) and handover history, we perform classification by embedding Support Vector Machines (SVMs) into an efficient pre-processing structure. Simulation results from a Manhattan Grid scenario and from a realistic radio map of downtown Frankfurt show that our system provides timely prediction at high accuracy.
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paper_title: Mobility Prediction in Wireless Ad Hoc Networks using Neural Networks

paper_content:

Mobility prediction allows estimating the stability of paths in a mobile wireless Ad Hoc networks. Identifying stable paths helps to improve routing by reducing the overhead and the number of connection interruptions. In this paper, we introduce a neural network based method for mobility prediction in Ad Hoc networks. This method consists of a multi-layer and recurrent neural network using back propagation through time algorithm for training.
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paper_title: Mobility Prediction in Mobile Ad Hoc Networks Using Extreme Learning Machines

paper_content:

Abstract Recent advances in wireless technology and computing have paved the way to the unprecedented rapid growth in de- mand and availability of mobile networking and services coupled with diverse system/network applications. Such advances triggered the emergence of future generation wireless networks and services to address the increasingly strin- gent requirements of quality-of-service (QoS) at various levels. The expected growth in wireless network activity and the number of wireless users will enable similar growth in bandwidth-crunching wireless applications to meet the QoS requirements. Mobility prediction of wireless users and units plays a major role in efficient planning and manage- ment of the bandwidth resources available in wireless networks. In return, this efficiency will allow better planning and improved overall QoS in terms of continuous service availability and efficient power management. In this paper, we propose extreme learning machines (ELMs), known for universal approximation, to model and predict mobility of arbitrary nodes in a mobile ad hoc network (MANET). MANETs use mobility prediction in location-aided routing and mobility aware topology control protocols. In these protocols, each mobile node is assumed to know its current mobility information (position, speed and movement direction angle). In this way, future node positions are predicted along with future distances between neighboring nodes. Unlike multilayer perceptrons (MLPs), ELMs capture better the existing interaction/correlation between the cartesian coordinates of the arbitrary nodes leading to more realistic and accurate mobility prediction based on several standard mobility models. Simulation results using standard mobility models illustrate how the proposed prediction method can lead to a significant improvement over conventional methods based on MLPs. Moreover, the proposed solution circumvents the prediction accuracy limitations in current algorithms when predicting future distances between neighboring nodes. The latter prediction is required by some applications like mobility aware topology control protocols.
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paper_title: Cloud Analytics for Wireless Metric Prediction - Framework and Performance

paper_content:

The knowledge of a future link quality for a user equipment (UE) in wireless networks may be used by the application and TCP layer so that user's experience is optimized. The metrics that are relevant are available at the core of the radio link and the application or TCP layers have no knowledge of the wireless metrics. With a signaling protocol to the application/TCP layer and a prediction mechanism of the future link quality the applications will be able to avoid buffer overflows and/or congestion. In this paper we identify metrics that are suitable for application/TCP control, and analyze the performance of the prediction of the metrics. We show that the prediction of wireless metrics can be made with low error (3-10% MAPE) even with high cloud latency. This is a significant result as it states that predictions in the cloud of even short term LTE metrics are possible and that the predictions are fairly accurate to influence the proper operation of applications/TCP in wireless environments.
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paper_title: Predicting Subscriber Dissatisfaction and Improving Retention in the Wireless Telecommunications Industry

paper_content:

We explore techniques from statistical machine learning to predict churn and, based on these predictions, to determine what incentives should be offered to subscribers to improve retention and maximize profitability to the carrier. The techniques include legit regression, decision trees, neural networks, and boosting. Our experiments are based on a database of nearly 47000 USA domestic subscribers and includes information about their usage, billing, credit, application, and complaint history. Our experiments show that under a wide variety of assumptions concerning the cost of intervention and the retention rate resulting from intervention, using predictive techniques to identify potential churners and offering incentives can yield significant savings to a carrier. We also show the importance of a data representation crafted by domain experts. Finally, we report on a real-world test of the techniques that validate our simulation experiments.
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paper_title: Kernel-Based Adaptive Online Reconstruction of Coverage Maps With Side Information

paper_content:

In this paper, we address the problem of reconstructing coverage maps from path-loss measurements in cellular networks. We propose and evaluate two kernel-based adaptive online algorithms as an alternative to typical offline methods. The proposed algorithms are application-tailored extensions of powerful iterative methods such as the adaptive projected subgradient method (APSM) and a state-of-the-art adaptive multikernel method. Assuming that the moving trajectories of users are available, it is shown how side information can be incorporated in the algorithms to improve their convergence performance and the quality of the estimation. The complexity is significantly reduced by imposing sparsity awareness in the sense that the algorithms exploit the compressibility of the measurement data to reduce the amount of data that is saved and processed. Finally, we present extensive simulations based on realistic data to show that our algorithms provide fast and robust estimates of coverage maps in real-world scenarios. Envisioned applications include path-loss prediction along trajectories of mobile users as a building block for anticipatory buffering or traffic offloading.
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paper_title: BreadCrumbs: forecasting mobile connectivity

paper_content:

Mobile devices cannot rely on a single managed network, but must exploit a wide variety of connectivity options as they travel. We argue that such systems must consider the derivative of connectivity--the changes inherent in movement between separately managed networks, with widely varying capabilities. With predictive knowledge of such changes, devices can more intelligently schedule network usage. To exploit the derivative of connectivity, we observe that people are creatures of habit; they take similar paths every day. Our system, BreadCrumbs, tracks the movement of the device's owner, and customizes a predictive mobility model for that specific user. Combined with past observations of wireless network capabilities, BreadCrumbs generates connectivity forecasts. We have built a BreadCrumbs prototype, and demonstrated its potential with several weeks of real-world usage. Our results show that these forecasts are sufficiently accurate, even with as little as one week of training, to provide improved performance with reduced power consumption for several applications.
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paper_title: Spatial Wireless Channel Prediction under Location Uncertainty

paper_content:

Spatial wireless channel prediction is important for future wireless networks, and in particular for proactive resource allocation at different layers of the protocol stack. Various sources of uncertainty must be accounted for during modeling and to provide robust predictions. We investigate two channel prediction frameworks, classical Gaussian processes (cGP) and uncertain Gaussian processes (uGP), and analyze the impact of location uncertainty during learning/training and prediction/testing, for scenarios where measurements uncertainty are dominated by large-scale fading. We observe that cGP generally fails both in terms of learning the channel parameters and in predicting the channel in the presence of location uncertainties.\textcolor{blue}{{} }In contrast, uGP explicitly considers the location uncertainty. Using simulated data, we show that uGP is able to learn and predict the wireless channel.
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paper_title: Combining local and global profiles for mobility prediction in LTE femtocells

paper_content:

We propose in this paper a mobility prediction model based on the notions of local and global mobile-user profiles. The local profiles are associated with a mobile user and correspond to its frequent and similar movements, whereas the global profiles match with the frequent and similar movements of the majority of users in the covered area. We consider the LTE network architecture with possible deployment of femtocells. The prediction model combines two complementary algorithms: the global profiles-based algorithm and the local profiles-based one. The former is implemented in the enhanced Node B and the home enhanced Node B and the latter works at the user terminal level. An algorithmic approach is used to identify such local and global profiles from real cellular network datasets and we show how to use them for an efficient mobility prediction. Simulation results show that our approach is significantly efficient in predicting both random and regular movements.
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paper_title: Characterizing and modeling internet traffic dynamics of cellular devices

paper_content:

Understanding Internet traffic dynamics in large cellular networks is important for network design, troubleshooting, performance evaluation, and optimization. In this paper, we present the results from our study, which is based upon a week-long aggregated flow level mobile device traffic data collected from a major cellular operator's core network. In this study, we measure and characterize the spatial and temporal dynamics of mobile Internet traffic. We distinguish our study from other related work by conducting the measurement at a larger scale and exploring mobile data traffic patterns along two new dimensions -- device types and applications that generate such traffic patterns. Based on the findings of our measurement analysis, we propose a Zipf-like model to capture the volume distribution of application traffic and a Markov model to capture the volume dynamics of aggregate Internet traffic. We further customize our models for different device types using an unsupervised clustering algorithm to improve prediction accuracy.
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paper_title: Mobility prediction-based smartphone energy optimization for everyday location monitoring

paper_content:

Monitoring a user's mobility during daily life is an essential requirement in providing advanced mobile services. While extensive attempts have been made to monitor user mobility, previous work has rarely addressed issues with battery lifetime in real deployment. In this paper, we introduce SmartDC, a mobility prediction-based adaptive duty cycling scheme to provide contextual information about a user's mobility: time-resolved places and paths. Unlike previous approaches that focused on minimizing energy consumption for tracking raw coordinates, we propose efficient techniques to maximize the accuracy of monitoring meaningful places with a given energy constraint. SmartDC comprises unsupervised mobility learner, mobility predictor, and Markov decision process-based adaptive duty cycling. SmartDC estimates the regularity of individual mobility and predicts residence time at places to determine efficient sensing schedules. Our experiment results show that SmartDC consumes 81% less energy than the periodic sensing schemes, and 87% less energy than a scheme employing context-aware sensing, yet it still correctly monitors 80% of a user's location changes within a 160-second delay.
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paper_title: Predicting mobile video inter-download times with Hidden Markov Models

paper_content:

Saving energy in mobile networks can be achieved by intelligently deactivating basestations. One way to know when to activate or deactivate basestations is understanding user behavior. In this paper, we look at the segment download behavior of users of mobile video streaming in adaptive bit-rate streaming systems with segmented downloads. From a large trace of HTTP requests we extract mobile video sessions and their inter-download times. We evaluate if Hidden Markov Models are feasible to model and predict when a user will request segments in the future. We further analyse how choosing model parameters influences the prediction quality.
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paper_title: Modeling steady-state and transient behaviors of user mobility: formulation, analysis, and application

paper_content:

Recent studies on mobility modeling have focused on characterizing user mobility from real traces of wireless LANs (WLANs)and creating mobility models based on such characterization. However, most of the work does not study how user mobility is correlated in time at different time scales. For example, the future APs with which a user will be associated are predicted without the knowledge of when the association will take place and for how long. In this paper, we build a mathematical model for characterizing both steady state and transient behaviors of user mobility in WLANs. Specifically, we mode user mobility by a semi-Markov process, and obtain the transition probability matrix and the sojourn time distribution from the association history of WLAN users available at Dartmouth college [21]. With the steady-state characterization of user mobility in WLANs, we can estimate the long-term wireless network usage among different access points. By comparing the steady-state distributions of semi-Markov models built based on trace data collected at different time scales, we are able to characterize the degree of correlation in time and location.We also perform a transient behavior analysis of the semi-Markov process (that characterizes user mobility), and devise a timed location prediction algorithm that accurately predicts the future locations of users both the future access points they will associate themselves with and the association duration. We demonstrate the utility of timed location prediction, by showing how it can be utilized to predict the distribution of future user ocations with the time information figured in, and redistributing loads among neighboring APs. An improvement of 80% (in terms of load balance) is observed in a wide spectrum of traffic loads in the simulation.
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paper_title: (Not) yet another policy for scalable video delivery to mobile users

paper_content:

In this work, we provide a methodology to analyze optimal adaptation policies for scalable video delivery in mobile environments. Typically, download policies for adaptive video are tuned to very specific system settings. The aim of this work is not to propose a new policy, but instead to understand how the optimal policy changes according to the operating environment and the system characteristics of a mobile video client. Armed with this insight, we can design or adapt policies for SVC adaptive video delivery for a broader range of settings. Using a semi-Markov decision process (SMDP), we find optimal video retrieval policies for a single user, subject to different limits on buffer capacity and different wireless environments. We apply a decision tree classifier to the output of the SMDP to derive simple approximate policies for 55 scenarios and use these to derive high-level rules on the relationship between optimal download policy and the underlying channel settings. For example, we show that the optimal policy is more conservative in slowly varying channels, and becomes more greedy in fast changing channels, and that instantaneous channel state is relevant to the decision-making process only in a setting with a very limited buffer capacity and slow-varying channel.
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paper_title: Channel gain prediction in wireless networks based on spatial-temporal correlation

paper_content:

Due to the popularity of GPS-enabled Smartphones the location of mobile terminals has become widely available [1]. Aided by such location information, we propose a general model to predict the channel gain of mobile users for multiple time steps in advance. Our model exploits spatial and temporal correlation in a Bayesian framework. The framework is composed of an autoregressive process and, according to our analysis of Rayleigh fading, of a multivariate Gaussian process. Numerical results shows that the proposed algorithm (i) achieves much higher accuracy than autoregression, especially for long-term prediction, and (ii) is substantially more robust than support vector machines against localization errors.
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paper_title: Application of Mobility Prediction in Wireless Networks Using Markov Renewal Theory

paper_content:

An understanding of the network traffic behavior is essential in the evolution of today's wireless networks and thus leads to a more efficient planning and management of the network's scarce bandwidth resources. Prior reservation of radio resources at future locations of a user's mobile trajectory can assist in optimizing the allocation of the network's limited resources and sustaining a desirable quality-of-service (QoS) level. This can also help to ensure that the network service can be available anywhere and anytime, which is only possible if, at any time, we can predict from where a user is going to make its demands. In this paper, we apply Markov renewal processes for both mobility modeling and predicting the likelihoods of the next-cell transition, along with anticipating the duration between the transitions, for an arbitrary user in a wireless network. Our proposed prediction technique will also be extended to compute the likelihoods of a user being in a particular state after N transitions. The proposed technique can also be used to estimate the expected spatial-temporal traffic load and activity at each location in a network's coverage area. Using some real traffic data, we illustrate how our proposed prediction method can lead to a significant improvement over some of the conventional methods.
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paper_title: Evaluating mobility models for temporal prediction with high-granularity mobility data

paper_content:

A mobility model is an essential requirement in accurately predicting an individual's future location. While extensive studies have been conducted to predict human mobility, previous work used coarse-grained mobility data with limited ability to capture human movements at a fine-grained level. In this paper, we empirically analyze several mobility models for predicting temporal behavior of an individual user. Unlike previous approaches, which employed coarse-grained mobility data with partial temporal-coverage, we use fine-grained and continuous mobility data for the evaluation of mobility models.We explore the regularity and predictability of human mobility, and evaluate location-dependent and location-independent models with several feature-aided schemes. Our experimental results show that a location-dependent predictor is better than a location-independent predictor for predicting temporal behavior of individual user. The duration of stay at a location is strongly correlated to the arrival time at the current location and the return-tendency to the next location, rather than recent k location sequences.We also find that false-positive predictions can be reduced by adaptive use of mobility models.
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paper_title: When and where next: individual mobility prediction

paper_content:

The ability to predict when an individual mobile user will leave his current location and where we will move next enables a myriad of qualitatively different Location-Based Services (LBSes) and applications. To this extent, the present paper proposes a statistical method that explicitly performs these related temporal and spatial prediction tasks in three continuous, sequential phases. In the first phase, the method continuously extracts grid-based staytime statistics from the GPS coordinate stream of the location-aware mobile device of the user. In the second phase, from the grid-based staytime statistics, the method periodically extracts and manages regions that the user frequently visits. Finally, in the third phase, from the stream of region-visits, the method continuously estimates parameters for an inhomogeneous continuous-time Markov model and in a continuous fashion predicts when the user will leave his current region and where he will move next. Empirical evaluations, using a number of long, real world trajectories from the Geo-Life data set, show that the proposed method outperforms a state-of-the-art, rule-based trajectory predictor both in terms of temporal and spatial prediction accuracy.
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paper_title: Mobility Prediction Using Mobile User Profiles

paper_content:

In this paper we propose a new approach for mobility prediction. It is based on the notion of mobile-user profile which corresponds to frequent similar movements of a user. Such a profile is defined, in the neighbourhood graph of a cellular network, as a set of similar sequences of crossed cells from one source cell to one destination cell. We propose an algorithmic approach to identify such profiles from real cellular network datasets and we show how to use them for an efficient mobility prediction. Simulation results show that the global approach is significantly efficient in predicting both random and regular movements.
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paper_title: Energy-Efficient Adaptive Video Transmission: Exploiting Rate Predictions in Wireless Networks

paper_content:

The unprecedented growth of mobile video traffic is adding significant pressure to the energy drain at both the network and the end user. Energy-efficient video transmission techniques are thus imperative to cope with the challenge of satisfying user demand at sustainable costs. In this paper, we investigate how predicted user rates can be exploited for energy-efficient video streaming with the popular Hypertext Transfer Protocol (HTTP)-based adaptive streaming (AS) protocols [e.g., dynamic adaptive streaming over HTTP (DASH)]. To this end, we develop an energy-efficient predictive green streaming (PGS) optimization framework that leverages predictions of wireless data rates to achieve the following objectives: 1) Minimize the required transmission airtime without causing streaming interruptions; 2) minimize total downlink base station (BS) power consumption for cases where BSs can be switched off in deep sleep; and 3) enable a tradeoff between AS quality and energy consumption. Our framework is first formulated as mixed-integer linear programming (MILP) where decisions on multiuser rate allocation, video segment quality, and BS transmit power are jointly optimized. Then, to provide an online solution, we present a polynomial-time heuristic algorithm that decouples the PGS problem into multiple stages. We provide a performance analysis of the proposed methods by simulations, and numerical results demonstrate that the PGS framework yields significant energy savings.
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paper_title: SmarterPhones: Anticipatory download scheduling for wireless video streaming

paper_content:

Video streaming is in high demand by mobile users. In cellular networks, however, the unreliable wireless channel leads to two major problems. Poor channel states degrade video quality and interrupt the playback when a user cannot sufficiently fill its local playout buffer: buffer underruns occur. In contrast, good channel conditions cause common greedy buffering schemes to buffer too much data. Such over-buffering wastes expensive wireless channel capacity. Assuming that we can anticipate future data rates, we plan the quality and download time of video segments ahead. This anticipatory download scheduling avoids buffer underruns by downloading a large number of segments before a drop in available data rate occurs, without wasting wireless capacity by excessive buffering. We developed a practical anticipatory scheduling algorithm for segmented video streaming protocols (e.g., HLS or MPEG DASH). Simulation results and testbed measurements show that our solution essentially eliminates playback interruptions without significantly decreasing video quality.
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paper_title: Anticipatory Admission Control and Resource Allocation for Media Streaming in Mobile Networks

paper_content:

The exponential growth of media streaming traffic will have a strong impact on the bandwidth consumption of the future wireless infrastructure. One key challenge is to deliver services taking into account the stringent requirements of mobile video streaming, e.g., the users' expected Quality-of-Service. Admission control and resource allocation can strongly benefit from the use of anticipatory information such as the prediction of future user's demand and expected channel gain. In this paper, we use this information to formulate an optimal admission control scheme that maximizes the number of accepted users into the system with the constraint that not only the current but also the expected demand of all users must be satisfied. Together with the optimal set of accepted users, the optimal resource scheduling is derived. In order to have a solution that can be computed in a reasonable time, we propose a low complexity heuristic. Numerical results show the performance of the proposed scheme with respect to the state of the art.
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paper_title: ⋄ Convex Optimization,

paper_content:

Convex optimization problems arise frequently in many different fields. A comprehensive introduction to the subject, this book shows in detail how such problems can be solved numerically with great efficiency. The focus is on recognizing convex optimization problems and then finding the most appropriate technique for solving them. The text contains many worked examples and homework exercises and will appeal to students, researchers and practitioners in fields such as engineering, computer science, mathematics, statistics, finance, and economics.
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paper_title: Rate Adaptation and Admission Control for Video Transmission With Subjective Quality Constraints

paper_content:

Adapting video data rate during streaming can effectively reduce the risk of playback interruptions caused by channel throughput fluctuations. The variations in rate, however, also introduce video quality fluctuations and thus potentially affects viewers' Quality of Experience (QoE). We show how the QoE of video users can be improved by rate adaptation and admission control. We conducted a subjective study wherein we found that viewers' QoE was strongly correlated with the empirical cumulative distribution function (eCDF) of the predicted video quality. Based on this observation, we propose a rate-adaptation algorithm that can incorporate QoE constraints on the empirical cumulative quality distribution per user. We then propose a threshold-based admission control policy to block users whose empirical cumulative quality distribution is not likely to satisfy their QoE constraint. We further devise an online adaptation algorithm to automatically optimize the threshold. Extensive simulation results show that the proposed scheme can reduce network resource consumption by 40% over conventional average-quality maximized rate-adaptation algorithms.
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paper_title: THEORY OF LINEAR AND INTEGER PROGRAMMING

paper_content:

The iterated difference of polyhedra $V = P_1 \backslash ( P_2 \backslash (... P_k ) ... )$ has been proposed independently in [Zwie-Aart-Wess92] and [Shon93] as a sufficient condition for $V$ to be exactly computable by a two-layered neural network. An algorithm checking whether $V$ included in $R^d$ is an iterated difference of polyhedra is proposed in [Zwie-Aart-Wess92]. However, this algorithm is not practically usable because it has a high computational complexity and it was only conjectured to stop with a negative answer when applied to a region which is not an iterated difference of polyhedra. This paper sheds some light on the nature of iterated difference of polyhedra. The outcomes are\,: (i) an algorithm which always stops after a small number of iterations, (ii) sufficient conditions for this algorithm to be polynomial and (iii) the proof that an iterated difference of polyhedra can be exactly computed by a two-layered neural network using only essential hyperplanes.
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paper_title: A Min–Max Model Predictive Control Approach to Robust Power Management in Ambulatory Wireless Sensor Networks

paper_content:

This paper addresses the problem of transmission power control within a network of resource-constrained wireless sensors that operate within a particular ambient healthcare environment. Sensor data transmitted to a remote base station within the network arrive subject to node location, orientation, and movement. Power is optimally allocated to all channels using a novel resource efficient algorithm. The proposed algorithm is based on a computationally efficient min-max model predictive controller that uses an uncertain linear state-space model of the tracking error that is estimated via local received signal strength feedback. An explicit solution for the power controller is computed offline using a multiparametric quadratic solver. It is shown that the proposed design leads to a robust control law that can be implemented quite readily on a commercial sensor node platform where computational and memory resources are extremely limited. The design is validated using a fully IEEE 802.15.4 compliant testbed using Tmote Sky sensor nodes mounted on fully autonomous MIABOT Pro miniature mobile robots. A repeatable representative selection of scaled ambulatory scenarios is presented that is quite typical of the data that will be generated in this space. The experimental results illustrate that the algorithm performs optimal power assignments, thereby ensuring a balance between energy consumption and a particular outage-based quality of service requirement while robustly compensating for disturbance uncertainties such as channel fading, interference, quantization error, noise, and nonlinear effects.
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paper_title: Generalized Predictive Control in a Wireless Networked Control System

paper_content:

The NCS (networked control system) is different from the conventional control systems which is the integration of the automation and control over communication network. When an NCS operates over the communication network, one of the major challenges is the network-induced delay in data transfer among the controllers, actuators, and sensors. This delay degrades system performance and causes system unstablility. This paper proposes a GPC (generalized predictive control) with the Kalman state estimator to compensate for the network-induced delay and packet loss. The GPC is implemented in WiNCS (Wireless NCS) based on IEEE 802.11 standard. An analytical NCS model and NS2 (network simulator version 2) are developed to simulate and evaluate the performance under the effect of various delays and packet loss rates. The result shows that the proposed GPC is adaptive and robust to the uncertainties in a time-delay system. The WiNCS is evaluated with latency and throughput measurements in various environments. The experiment setup conforming to the IEEE 802.11 standard achieves an average latency of 1.3 ms and a data throughput of 3.000 kB/s up to a distance of 70 m. The results demonstrate the feasibility of real-time closed-loop control with the proposed concept.
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paper_title: A survey of industrial model predictive control technology

paper_content:

This paper provides an overview of commercially available model predictive control (MPC) technology, both linear and nonlinear, based primarily on data provided by MPC vendors. A brief history of industrial MPC technology is presented first, followed by results of our vendor survey of MPC control and identification technology. A general MPC control algorithm is presented, and approaches taken by each vendor for the different aspects of the calculation are described. Identification technology is reviewed to determine similarities and differences between the various approaches. MPC applications performed by each vendor are summarized by application area. The final section presents a vision of the next generation of MPC technology, with an emphasis on potential business and research opportunities. r 2002 Elsevier Science Ltd. All rights reserved.
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paper_title: Networked model predictive traffic control with time varying optimization horizon: The Grenoble South Ring case study

paper_content:

This work discusses the design of a networked traffic control scheme. We refer to the Grenoble South Ring traffic system, as case study, in which the control actions are computed in a control centre far from the traffic system and then sent, through a wireless communication channel, to the actuators placed along the road, i.e. on-ramp traffic lights in the considered case of ramp metering control. The communication channel is affected by delays and packet loss. In order to counteract the effects due to the transmission of the variable over the communication channel, we suggest to adopt a model predictive control (MPC) strategy based on the use of a buffer. Moreover, in order to limit the computational burden and improve the effectiveness of the proposal, the length of the optimization horizon of the predictive control algorithm, and, consequently, the buffer length, is updated relying on a delay estimation. The performances of the proposed approach are assessed in simulation relying on a traffic model the parameters of which are identified using data produced by a commercial microscopic simulator of the Grenoble South Ring traffic system. The capabilities of the considered control scheme when the system parameters vary and the transmitted signals are affected by time delays are highlighted.
---
paper_title: (Not) yet another policy for scalable video delivery to mobile users

paper_content:

In this work, we provide a methodology to analyze optimal adaptation policies for scalable video delivery in mobile environments. Typically, download policies for adaptive video are tuned to very specific system settings. The aim of this work is not to propose a new policy, but instead to understand how the optimal policy changes according to the operating environment and the system characteristics of a mobile video client. Armed with this insight, we can design or adapt policies for SVC adaptive video delivery for a broader range of settings. Using a semi-Markov decision process (SMDP), we find optimal video retrieval policies for a single user, subject to different limits on buffer capacity and different wireless environments. We apply a decision tree classifier to the output of the SMDP to derive simple approximate policies for 55 scenarios and use these to derive high-level rules on the relationship between optimal download policy and the underlying channel settings. For example, we show that the optimal policy is more conservative in slowly varying channels, and becomes more greedy in fast changing channels, and that instantaneous channel state is relevant to the decision-making process only in a setting with a very limited buffer capacity and slow-varying channel.
---
paper_title: Reinforcement Learning: An Introduction

paper_content:

Reinforcement learning, one of the most active research areas in artificial intelligence, is a computational approach to learning whereby an agent tries to maximize the total amount of reward it receives when interacting with a complex, uncertain environment. In Reinforcement Learning, Richard Sutton and Andrew Barto provide a clear and simple account of the key ideas and algorithms of reinforcement learning. Their discussion ranges from the history of the field's intellectual foundations to the most recent developments and applications. The only necessary mathematical background is familiarity with elementary concepts of probability. The book is divided into three parts. Part I defines the reinforcement learning problem in terms of Markov decision processes. Part II provides basic solution methods: dynamic programming, Monte Carlo methods, and temporal-difference learning. Part III presents a unified view of the solution methods and incorporates artificial neural networks, eligibility traces, and planning; the two final chapters present case studies and consider the future of reinforcement learning.
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paper_title: Bitrate adaptation for mobile video streaming based on buffer and channel state

paper_content:

Mobile video streaming is a rising business but poses significant challenges for network and service operators. In mobile networks, time-variant wireless channels often violate the rate requirement of the content, which leads to a stalled video stream. Common HTTP Adaptive Streaming techniques cannot effectively react to channel variation by adapting the video bitrate only to the state of the playback buffer. In this paper, we present an optimal solution that adapts to the current and predicted channel state as well. Our optimal formulation is based on Markov Decision Processes and studied with real content and channel traces from vehicular users. The results clearly show the large benefit of complementing buffer-aware by channel-aware adaptation and prediction. Finally, we present a heuristic that closely achieves optimal service quality at practical computational complexity.
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paper_title: Predictive Caching Framework for Mobile Wireless Networks

paper_content:

With increasing popularity of Netflix, Yahoo! Video, etc., interactive multimedia services such as video-on-demand (VoD) provide an interesting and rich field of research. The advent of smarter wireless devices has surged the need for such services through wireless connectivity. However, personalization of individual user needs, reducing latency, coupled with maintaining low operational costs provides a challenging problem. In this paper, we propose an efficient VoD system, for wireless mobile devices, based on a novel caching algorithm, Intelligent Network Caching Algorithm (INCA) using analytics-driven look ahead scheme for both prefetch and replacement policies to deliver higher performance. This enables enhanced Quality Of Experience (QoE) of users with limited infrastructural changes and low operational cost. Alongside, we develop theoretical formulation of the QoE optimization problem that lies at the intersection of MPC (Markov Predictive Control) and MDP (Markov Decision Process). Empirical analysis over realistic user video query logs demonstrate better cache hit rate and QoE with low prefetch bandwidth, compared to existing caching schemes.
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paper_title: A systematic framework for dynamically optimizing multi-user wireless video transmission

paper_content:

In this paper, we systematically formulate the problem of multi-user wireless video transmission as a multi-user Markov decision process (MUMDP) by explicitly considering the users' heterogeneous video traffic characteristics, time-varying network conditions as well as, importantly, the dynamic coupling among the users' resource allocations across time, which are often ignored in existing multi-user video transmission solutions. To comply with the decentralized wireless networks' architecture, we propose to decompose the MUMDP into multiple local MDPs using Lagrangian relaxation. Unlike in conventional multi-user video transmission solutions stemming from the network utility maximization framework, the proposed decomposition enables each wireless user to individually solve its own local MDP (i.e. dynamic single-user cross-layer optimization) and the network coordinator to update the Lagrangian multipliers (i.e. resource prices) based on not only current, but also the future resource needs of all users, such that the long-term video quality of all users is maximized. This MUMDP solution provides us the necessary foundations and structures for solving multiuser video communication problems. However, to implement this framework in practice requires statistical knowledge of the experienced environment dynamics, which is often unavailable before transmission time. To overcome this obstacle, we propose a novel online learning algorithm, which allows the wireless users to simultaneously update their policies at multiple states during each time slot. This is different from conventional learning solutions, which often update the current visited state per time slot. The proposed learning algorithm can significantly improve the learning performance, thereby dramatically improving the video quality experienced by the wireless users over time. Our simulation results demonstrate the efficiency of the proposed MUMDP framework as compared to conventional multi-user video transmission solutions.
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paper_title: A Markov Decision Model for Adaptive Scheduling of Stored Scalable Videos

paper_content:

We propose two scheduling algorithms that seek to optimize the quality of scalably coded videos that have been stored at a video server before transmission. The first scheduling algorithm is derived from a Markov decision process (MDP) formulation developed here. We model the dynamics of the channel as a Markov chain and reduce the problem of dynamic video scheduling to a tractable Markov decision problem over a finite-state space. Based on the MDP formulation, a near-optimal scheduling policy is computed that minimizes the mean square error. Using insights taken from the development of the optimal MDP-based scheduling policy, the second proposed scheduling algorithm is an online scheduling method that only requires easily measurable knowledge of the channel dynamics, and is thus viable in practice. Simulation results show that the performance of both scheduling algorithms is close to a performance upper bound also derived in this paper.
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paper_title: Matching theory for future wireless networks: fundamentals and applications

paper_content:

The emergence of novel wireless networking paradigms such as small cell and cognitive radio networks has forever transformed the way in which wireless systems are operated. In particular, the need for self-organizing solutions to manage the scarce spectral resources has become a prevalent theme in many emerging wireless systems. In this article, the first comprehensive tutorial on the use of matching theory, a Nobel Prize winning framework, for resource management in wireless networks is developed. To cater for the unique features of emerging wireless networks, a novel, wireless-oriented classification of matching theory is proposed. Then the key solution concepts and algorithmic implementations of this framework are exposed. The developed concepts are applied in three important wireless networking areas in order to demonstrate the usefulness of this analytical tool. Results show how matching theory can effectively improve the performance of resource allocation in all three applications discussed.
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paper_title: Context-Aware Scheduling of Joint Millimeter Wave and Microwave Resources for Dual-Mode Base Stations

paper_content:

One of the most promising approaches to overcome the drastic channel variations of millimeter wave (mmW) communications is to deploy dual-mode base stations that integrate both mmW and microwave (pW) frequencies. Reaping the benefits of a dual-mode operation requires scheduling mechanisms that can allocate resources efficiently and jointly at both frequency bands. In this paper, a novel resource allocation framework is proposed that exploits users' context, in terms of user application (UA) delay requirements, to maximize the quality-of-service (QoS) of a dual-mode base station. In particular, such a context-aware approach enables the network to dynamically schedule UAs, instead of users, thus providing more precise delay guarantees and a more efficient exploitation of the mmW resources. The scheduling of UAs is formulated as a one-to-many matching problem between UAs and resources and a novel algorithm is proposed to solve it. The proposed algorithm is shown to converge to a two-sided stable matching between UAs and network resources. Simulation results show that the proposed approach outperforms classical CSI-based scheduling in terms of the per UA QoS, yielding up to 36% improvement. The results also show that exploiting mmW resources provides significant traffic offloads reaching up to 43% from μW band.
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paper_title: Context-Aware Small Cell Networks: How Social Metrics Improve Wireless Resource Allocation

paper_content:

In this paper, a novel approach for optimizing and managing resource allocation in wireless small cell networks (SCNs) with device-to-device (D2D) communication is proposed. The proposed approach allows to jointly exploit both the wireless and social context of wireless users for optimizing the overall allocation of resources and improving traffic offload in SCNs. This context-aware resource allocation problem is formulated as a matching game in which user equipments (UEs) and resource blocks (RBs) rank one another, based on utility functions that capture both wireless and social metrics. Due to social interrelations, this game is shown to belong to a class of matching games with peer effects. To solve this game, a novel, selforganizing algorithm is proposed, using which UEs and RBs can interact to decide on their desired allocation. The proposed algorithm is then proven to converge to a two-sided stable matching between UEs and RBs. The properties of the resulting stable outcome are then studied and assessed. Simulation results using real social data show that clustering of socially connected users allows to offload a substantially larger amount of traffic than the conventional context-unaware approach. These results show that exploiting social context has high practical relevance in saving resources on the wireless links and on the backhaul.
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paper_title: A context-aware matching game for user association in wireless small cell networks

paper_content:

Small cell networks are seen as a promising technology for boosting the performance of future wireless networks. In this paper, we propose a novel context-aware user-cell association approach for small cell networks that exploits the information about the velocity and trajectory of the users while also taking into account their quality of service (QoS) requirements. We formulate the problem in the framework of matching theory with externalities in which the agents, namely users and small cell base stations (SCBSs), have strict interdependent preferences over the members of the opposite set. To solve the problem, we propose a novel algorithm that leads to a stable matching among the users and SCBSs. We show that the proposed approach can better balance the traffic among the cells while also satisfying the QoS of the users. Simulation results show that the proposed matching algorithm yields significant performance advantages relative to traditional context-unaware approaches.
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paper_title: Many-to-many matching games for proactive social-caching in wireless small cell networks

paper_content:

In this paper, we address the caching problem in small cell networks from a game theoretic point of view. In particular, we formulate the caching problem as a many-to-many matching game between small base stations and service providers' servers. The servers store a set of videos and aim to cache these videos at the small base stations in order to reduce the experienced delay by the end-users. On the other hand, small base stations cache the videos according to their local popularity, so as to reduce the load on the backhaul links. We propose a new matching algorithm for the many-to-many problem and prove that it reaches a pairwise stable outcome. Simulation results show that the number of satisfied requests by the small base stations in the proposed caching algorithm can reach up to three times the satisfaction of a random caching policy. Moreover, the expected download time of all the videos can be reduced significantly.
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paper_title: 5G cellular: key enabling technologies and research challenges

paper_content:

The evolving fifth generation (5G) cellular wireless networks are envisioned to provide higher data rates, enhanced end-user quality-of-experience (QoE), reduced end-to-end latency, and lower energy consumption. This article presents several emerging technologies, which will enable and define the 5G mobile communications standards. The major research problems, which these new technologies breed, as well as the measurement and test challenges for 5G systems are also highlighted.
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paper_title: Handbook of Wireless Networks and Mobile Computing

paper_content:

If you want to get Handbook of Internet Computing pdf eBook copy write by good Handbook of Wireless Networks and Mobile Computing Google Books. Mobile Computing General. Handbook of Algorithms for Wireless Networking and Mobile Computing by Azzedine Boukerche (Editor). Call Number: TK 5103.2. CITS4419 Mobile and Wireless Computing software projects related to wireless networks, (2) write technical reports and documentation for complex computer.
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paper_title: A Survey of Measurement-Based Spectrum Occupancy Modeling for Cognitive Radios

paper_content:

Spectrum occupancy models are very useful in cognitive radio designs. They can be used to increase spectrum sensing accuracy for more reliable operation, to remove spectrum sensing for higher resource usage efficiency, or to select channels for better opportunistic access, among other applications. In this survey, various spectrum occupancy models from measurement campaigns taken around the world are investigated. These models extract different statistical properties of the spectrum occupancy from the measured data. In addition to these models, spectrum occupancy prediction is also discussed, where autoregressive and/or moving-average models are used to predict the channel status at future time instants. After comparing these different methods and models, several challenges are also summarized based on this survey.
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paper_title: A Survey on Device-to-Device Communication in Cellular Networks

paper_content:

Device-to-device (D2D) communications was initially proposed in cellular networks as a new paradigm for enhancing network performance. The emergence of new applications such as content distribution and location-aware advertisement introduced new user cases for D2D communications in cellular networks. The initial studies showed that D2D communications has advantages such as increased spectral efficiency and reduced communication delay. However, this communication mode introduces complications in terms of interference control overhead and protocols that are still open research problems. The feasibility of D2D communications in Long-Term Evolution Advanced is being studied by academia, industry, and standardization bodies. To date, there are more than 100 papers available on D2D communications in cellular networks, but there is no survey on this field. In this paper, we provide a taxonomy based on the D2D communicating spectrum and review the available literature extensively under the proposed taxonomy. Moreover, we provide new insights into the over-explored and under-explored areas that lead us to identify open research problems of D2D communications in cellular networks.
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paper_title: Internet of Things in Industries: A Survey

paper_content:

Internet of Things (IoT) has provided a promising opportunity to build powerful industrial systems and applications by leveraging the growing ubiquity of radio-frequency identification (RFID), and wireless, mobile, and sensor devices. A wide range of industrial IoT applications have been developed and deployed in recent years. In an effort to understand the development of IoT in industries, this paper reviews the current research of IoT, key enabling technologies, major IoT applications in industries, and identifies research trends and challenges. A main contribution of this review paper is that it summarizes the current state-of-the-art IoT in industries systematically.
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paper_title: Internet of Things: A Survey on Enabling Technologies, Protocols, and Applications

paper_content:

This paper provides an overview of the Internet of Things (IoT) with emphasis on enabling technologies, protocols, and application issues. The IoT is enabled by the latest developments in RFID, smart sensors, communication technologies, and Internet protocols. The basic premise is to have smart sensors collaborate directly without human involvement to deliver a new class of applications. The current revolution in Internet, mobile, and machine-to-machine (M2M) technologies can be seen as the first phase of the IoT. In the coming years, the IoT is expected to bridge diverse technologies to enable new applications by connecting physical objects together in support of intelligent decision making. This paper starts by providing a horizontal overview of the IoT. Then, we give an overview of some technical details that pertain to the IoT enabling technologies, protocols, and applications. Compared to other survey papers in the field, our objective is to provide a more thorough summary of the most relevant protocols and application issues to enable researchers and application developers to get up to speed quickly on how the different protocols fit together to deliver desired functionalities without having to go through RFCs and the standards specifications. We also provide an overview of some of the key IoT challenges presented in the recent literature and provide a summary of related research work. Moreover, we explore the relation between the IoT and other emerging technologies including big data analytics and cloud and fog computing. We also present the need for better horizontal integration among IoT services. Finally, we present detailed service use-cases to illustrate how the different protocols presented in the paper fit together to deliver desired IoT services.
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paper_title: OSI Reference Model - The ISO Model of Architecture for Open Systems Interconnection

paper_content:

Considering the urgency of the need for standards which would allow constitution of heterogeneous computer networks, ISO created a new subcommittee for Open Systems Interconnection (ISO/TC97/SC16) in 1977. The first priority of subcommittee 16 was to develop an architecture for open systems interconnection which could serve as a framework for the definition of standard protocols. As a result of 18 months of studies and discussions, SC16 adopted a layered architecture comprising seven layers (Physical, Data Link, Network, Transport, Session, Presentation, and Application). In July 1979 the specifications of this architecture, established by SC16, were passed under the name of OSI Reference Model to Technical Committee 97 Data Processing along with recommendations to start officially, on this basis, a set of protocols standardization projects to cover the most urgent needs. These recommendations were adopted by TC97 at the end of 1979 as the basis for the following development of standards for Open Systems Interconnection within ISO. The OSI Reference Model was also recognized by CCITT Rapporteur's Group on Layered Model for Public Data Network Services. This paper presents the model of architecture for Open Systems Interconnection developed by SC16. Some indications are also given on the initial set of protocols which will likely be developed in this OSI Reference Model.
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paper_title: OSI Reference Model - The ISO Model of Architecture for Open Systems Interconnection

paper_content:

Considering the urgency of the need for standards which would allow constitution of heterogeneous computer networks, ISO created a new subcommittee for Open Systems Interconnection (ISO/TC97/SC16) in 1977. The first priority of subcommittee 16 was to develop an architecture for open systems interconnection which could serve as a framework for the definition of standard protocols. As a result of 18 months of studies and discussions, SC16 adopted a layered architecture comprising seven layers (Physical, Data Link, Network, Transport, Session, Presentation, and Application). In July 1979 the specifications of this architecture, established by SC16, were passed under the name of OSI Reference Model to Technical Committee 97 Data Processing along with recommendations to start officially, on this basis, a set of protocols standardization projects to cover the most urgent needs. These recommendations were adopted by TC97 at the end of 1979 as the basis for the following development of standards for Open Systems Interconnection within ISO. The OSI Reference Model was also recognized by CCITT Rapporteur's Group on Layered Model for Public Data Network Services. This paper presents the model of architecture for Open Systems Interconnection developed by SC16. Some indications are also given on the initial set of protocols which will likely be developed in this OSI Reference Model.
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paper_title: 5G cellular: key enabling technologies and research challenges

paper_content:

The evolving fifth generation (5G) cellular wireless networks are envisioned to provide higher data rates, enhanced end-user quality-of-experience (QoE), reduced end-to-end latency, and lower energy consumption. This article presents several emerging technologies, which will enable and define the 5G mobile communications standards. The major research problems, which these new technologies breed, as well as the measurement and test challenges for 5G systems are also highlighted.
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paper_title: Optimizing stored video delivery for mobile networks: The value of knowing the future

paper_content:

This paper considers the design of cross-layer opportunistic transport for stored video over wireless networks with a slow varying (average) capacity. We focus on two key ideas: (1) scheduling data transmissions when capacity is high; and (2), exploiting knowledge of future capacity variations. The latter is possible when users' mobility is known or predictable, e.g., users riding on public transportation or using navigation systems. We consider the design of cross-layer transmission schedules which minimize system utilization (and thus possibly transmit/receive energy) while avoiding, if at all possible, rebuffering/delays, in several scenarios. For the single-user anticipative case where all future capacity variations are known beforehand; we establish the optimal transmission schedule is a Generalized Piecewise Constant Thresholding (GPCT) scheme. For the single-user partially anticipative case where only a finite window of future capacity variations is known, we propose an online Greedy Fixed Horizon Control (GFHC). An upper bound on the competitive ratio of GFHC and GPCT is established showing how performance loss depends on the window size, receiver playback buffer, and capacity variability. Finally we consider the multiuser case where we can exploit both future temporal and multiuser diversity. Our simulations and evaluation based on a measured wireless capacity trace exhibit robust potential gains for our proposed transmission schemes.
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paper_title: Anticipatory quality-resource allocation for multi-user mobile video streaming

paper_content:

Mobile video delivery forms the largest part of the traffic in cellular networks. Thus optimizing the resource allocation to satisfy a user's quality of experience is becoming paramount in modern communications. This paper belongs to the line of research known as anticipatory networking that makes use of prediction of wireless capacity to improve communication performance. In particular, we focus on the problem of optimal resource allocation for steady video delivery under maximum average quality constraints for multiple users. We formulate the problem as a piecewise linear program and provide a heuristic algorithm, which solution is close to optimal. Based on our formulation we are now able to trade off minimum video quality, average quality and offered network capacity.
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paper_title: Anticipation of ETX Metric to Manage Mobility in Ad Hoc Wireless Networks

paper_content:

When a node is moving in a wireless network, the routing metrics associated to its wireless links may reflect link quality degradations and help the routing process to adapt its routes. Unfortunately, an important delay between the metric estimation and its inclusion in the routing process makes this approach inefficient. In this paper, we introduce an algorithm that predicts metric values a few seconds in advance, in order to compensate the delay involved by the link quality measurement and their dissemination by the routing protocol. We consider classical metrics, in particular ETX Expected Transmission Count and ETT Expected Transmission Time, but we combine their computations to our prediction algorithm. Extensive simulations show the route enhancement as the Packet Delivery Ratio PDR is close to 1 in presence of mobility.
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paper_title: Living on the edge: The role of proactive caching in 5G wireless networks

paper_content:

This article explores one of the key enablers of beyond 4G wireless networks leveraging small cell network deployments, proactive caching. Endowed with predictive capabilities and harnessing recent developments in storage, context awareness, and social networks, peak traffic demands can be substantially reduced by proactively serving predictable user demands via caching at base stations and users' devices. In order to show the effectiveness of proactive caching, we examine two case studies that exploit the spatial and social structure of the network, where proactive caching plays a crucial role. First, in order to alleviate backhaul congestion, we propose a mechanism whereby files are proactively cached during off-peak periods based on file popularity and correlations among user and file patterns. Second, leveraging social networks and D2D communications, we propose a procedure that exploits the social structure of the network by predicting the set of influential users to (proactively) cache strategic contents and disseminate them to their social ties via D2D communications. Exploiting this proactive caching paradigm, numerical results show that important gains can be obtained for each case study, with backhaul savings and a higher ratio of satisfied users of up to 22 and 26 percent, respectively. Higher gains can be further obtained by increasing the storage capability at the network edge.
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paper_title: Evaluating mobile signal and location predictability along public transportation routes

paper_content:

Emerging mobility-aware content delivery ap- proaches are being proposed to cope with the increasing usage of data from vehicular users. The main idea is to forecast the user locations and associated link capacity, and then proactively counter service fluctuations in advance. For instance, a user that is heading towards low coverage can be prioritized and have video content prebuffered. While the reported gains are encouraging, the results are primarily based on assumptions of perfect prediction. Investigating the predictability of mobility and future signal variations is therefore imperative to evaluate the practical viability of such predictive content delivery paradigms. To this end, this paper presents a large-scale measurement study of 33 repeated trips along a 23.4km bus route covering urban and sub-urban areas in Kingston, Canada. We provide a thorough analysis of the collected traces to investigate the effects of geographical area, time, forecasting window, and contextual factors such as signal lights and bus stops. The collected dataset can also be used in several other ways to further investigate and drive research in predictive vehicular content delivery.
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paper_title: Human mobility prediction based on individual and collective geographical preferences

paper_content:

Understanding and predicting human mobility is a crucial component of transportation planning and management. In this paper we propose a new model to predict the location of a person over time based on individual and collective behaviors. The model is based on the person's past trajectory and the geographical features of the area where the collectivity moves, both in terms of land use, points of interests and distance of trips. The effectiveness of the proposed prediction model is tested using a massive mobile phone location dataset available for the Boston metropolitan area. Experimental results show good levels of accuracy in terms of prediction error and prove the advantage of using the collective behavior in the prediction model.
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paper_title: SmarterPhones: Anticipatory download scheduling for wireless video streaming

paper_content:

Video streaming is in high demand by mobile users. In cellular networks, however, the unreliable wireless channel leads to two major problems. Poor channel states degrade video quality and interrupt the playback when a user cannot sufficiently fill its local playout buffer: buffer underruns occur. In contrast, good channel conditions cause common greedy buffering schemes to buffer too much data. Such over-buffering wastes expensive wireless channel capacity. Assuming that we can anticipate future data rates, we plan the quality and download time of video segments ahead. This anticipatory download scheduling avoids buffer underruns by downloading a large number of segments before a drop in available data rate occurs, without wasting wireless capacity by excessive buffering. We developed a practical anticipatory scheduling algorithm for segmented video streaming protocols (e.g., HLS or MPEG DASH). Simulation results and testbed measurements show that our solution essentially eliminates playback interruptions without significantly decreasing video quality.
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paper_title: (Not) yet another policy for scalable video delivery to mobile users

paper_content:

In this work, we provide a methodology to analyze optimal adaptation policies for scalable video delivery in mobile environments. Typically, download policies for adaptive video are tuned to very specific system settings. The aim of this work is not to propose a new policy, but instead to understand how the optimal policy changes according to the operating environment and the system characteristics of a mobile video client. Armed with this insight, we can design or adapt policies for SVC adaptive video delivery for a broader range of settings. Using a semi-Markov decision process (SMDP), we find optimal video retrieval policies for a single user, subject to different limits on buffer capacity and different wireless environments. We apply a decision tree classifier to the output of the SMDP to derive simple approximate policies for 55 scenarios and use these to derive high-level rules on the relationship between optimal download policy and the underlying channel settings. For example, we show that the optimal policy is more conservative in slowly varying channels, and becomes more greedy in fast changing channels, and that instantaneous channel state is relevant to the decision-making process only in a setting with a very limited buffer capacity and slow-varying channel.
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paper_title: Mining User Mobility Features for Next Place Prediction in Location-Based Services

paper_content:

Mobile location-based services are thriving, providing an unprecedented opportunity to collect fine grained spatio-temporal data about the places users visit. This multi-dimensional source of data offers new possibilities to tackle established research problems on human mobility, but it also opens avenues for the development of novel mobile applications and services. In this work we study the problem of predicting the next venue a mobile user will visit, by exploring the predictive power offered by different facets of user behavior. We first analyze about 35 million check-ins made by about 1 million Foursquare users in over 5 million venues across the globe, spanning a period of five months. We then propose a set of features that aim to capture the factors that may drive users' movements. Our features exploit information on transitions between types of places, mobility flows between venues, and spatio-temporal characteristics of user check-in patterns. We further extend our study combining all individual features in two supervised learning models, based on linear regression and M5 model trees, resulting in a higher overall prediction accuracy. We find that the supervised methodology based on the combination of multiple features offers the highest levels of prediction accuracy: M5 model trees are able to rank in the top fifty venues one in two user check-ins, amongst thousands of candidate items in the prediction list.
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paper_title: The Tactile Internet: Applications and Challenges

paper_content:

Wireless communications today enables us to connect devices and people for an unprecedented exchange of multimedia and data content. The data rates of wireless communications continue to increase, mainly driven by innovation in electronics. Once the latency of communication systems becomes low enough to enable a round-trip delay from terminals through the network back to terminals of approximately 1 ms, an overlooked breakthrough?human tactile to visual feedback control?will change how humans communicate around the world. Using these controls, wireless communications can be the platform for enabling the control and direction of real and virtual objects in many situations of our life. Almost no area of the economy will be left untouched, as this new technology will change health care, mobility, education, manufacturing, smart grids, and much more. The Tactile Internet will become a driver for economic growth and innovation and will help bring a new level of sophistication to societies.
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paper_title: Wireless resource sharing for multiple operators: Generalization, fairness, and the value of prediction

paper_content:

Abstract Sharing wireless channel resources among cellular network operators saves cost by efficiently utilizing hardware, sites, and spectrum. To achieve high spectral efficiency, resource sharing has to adapt to the instantaneous load and channel state. However, before operators accept such dynamic scheduling policies, a solid understanding of their reliability and performance is required. In this paper, we present a consistent theoretical framework for Multi-Operator Scheduling (MOS). This formulation allows to analyze sharing guarantees and spectral efficiency for a large number of parameters and covers various fixed and dynamic resource sharing policies as special cases. Extending our study to the complete knowledge of the future channel state allows us to study the complete performance region of MOS. The proofs, analysis and simulation results in this work provide a profound understanding of the fundamental tradeoff between sharing guarantees and performance in multi-operator sharing. This insight will enable reliable yet efficient infrastructure sharing in 5G Radio Access Networks.
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paper_title: Reliability in future radio access networks: From linguistic to quantitative definitions

paper_content:

For the first time since the advent of mobile networks, the idea of advancing their pervasiveness by co-opting them into most aspects of daily life has taken hold and this idea is, henceforth, intended to be a mainstay of future networks (5G and beyond). As a result, a term one frequently encounters in the latest literature pertinent to radio access networks is reliability. It is, however, fairly evident that it is mostly used in a colloquial linguistic sense or that, in some cases, it is used synonymously with availability. This work is, to the best of our knowledge, the first to provide a quantitative definition of reliability which stems from its characterization in the dictionary and is based on quantifiable definitions of resilience, availability, and other parameters important to radio access networks. The utility of this quantitative definition is demonstrated by developing a reliability-aware scheduler which takes predictions of the channel quality into account. The scheduler developed here is also compared with the classical proportional fair scheduler in use today. This comparison not only succeeds in highlighting the practicality of the definition provided, but it also shows that the anticipatory reliability-aware scheduler is able to provide an improvement of about 35 – 50% in reliability when compared to a proportional fair scheduler which is common in contemporary use.
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paper_title: Context-aware vehicular cyber-physical systems with cloud support: architecture, challenges, and solutions

paper_content:

The advances in wireless communication techniques, mobile cloud computing, and context- aware technologies boost a growing interest in the design, development, and deployment of vehicular networks for emerging applications. This leads to an increasing evolutionary tendency to change from vehicular networks toward cloud-assisted context-aware vehicular cyberphysical systems. In this article, we first propose a multi-layered context-aware architecture and introduce two crucial service components, vehicular social networks and context-aware vehicular security. Then we propose an application scenario regarding the context-aware dynamic parking services by illuminating the cloud-assisted architecture and logic flow. Finally, we investigate the challenges and possible solutions, including context-aware safety hazard prediction, context-aware dynamic vehicle routing, and context-aware vehicular clouds.
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Title: A Survey of Anticipatory Mobile Networking: Context-Based Classification, Prediction Methodologies, and Optimization Techniques

Section 1: Background and Guidelines
Description 1: This section provides an introduction to anticipatory networking, outlining key attributes such as context, prediction, and optimization, and explaining their influence on networking solutions. It also introduces the organization of the survey.

Section 2: Related Work
Description 2: This section discusses recent surveys on topics related to anticipatory networking, comparing different approaches and highlighting their contributions to the field.

Section 3: A Context-Based Classification of Anticipatory Networking Solutions
Description 3: This section classifies anticipatory networking solutions based on the types of context they exploit, such as geographic, link, traffic, and social contexts, and describes the predictive techniques used.

Section 4: Prediction Methodologies for Anticipatory Networking
Description 4: This section presents various prediction methods adopted in anticipatory networking, including time series predictive modeling, similarity-based classification, regression analysis, and statistical methods for probabilistic forecasting.

Section 5: Optimization Techniques for Anticipatory Networking
Description 5: This section identifies the main optimization techniques used in anticipation-driven networking solutions, such as convex optimization, model predictive control, Markov decision processes, and game theoretic approaches.

Section 6: Applicability of Anticipatory Networking to Other Wireless Networks
Description 6: This section explores how different types of mobile wireless networks, such as 5G, MANETs, CR networks, D2D communications, and IoT, could benefit from anticipatory networking solutions.

Section 7: On the Impact of Anticipatory Networking on the Protocol Stack
Description 7: This section examines the impact of anticipatory networking on different layers of the ISO/OSI protocol stack and discusses how predictive techniques can be integrated at each layer to enhance network performance.

Section 8: Issues, Challenges, and Research Directions
Description 8: This section outlines the main issues and challenges that need to be addressed for anticipatory networking to be successfully implemented in 5G networks. It also discusses potential research directions in this field.

Section 9: Conclusion
Description 9: This section summarizes the key takeaways from the survey and reflects on the current state and future prospects of anticipatory networking in mobile networks. It also highlights the need for a comprehensive cost-benefit analysis to better understand the trade-offs involved.