Fog Computing for Detecting Vehicular Congestion, An Internet of Vehicles based Approach: A review
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paper_title: Internet of Things Based Solutions for Road Safety and Traffic Management in Intelligent Transportation Systems

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Road safety, traffic congestion and efficiency of the transport sector are major global concerns. Improving this is the primary objective of intelligent transport systems (ITS). Having Internet of things (IoT) based solutions for ITS would enable motorists to obtain prior contextual guidance to reduce congestion and avoid potential hazards. IoT based solutions enabling collection of data from client nodes in a wireless sensor network in the transport environment implementing ITS goals is studied. The parameters to be monitored, type of sensors and communication related design parameters are identified to develop an effective IoT based solution. Road safety techniques studied include distance sensing, improper driving detection and accident prevention, weather related events and negligent driving detection and accident avoidance. Vehicle to vehicle communication and vehicle to infrastructure based channels are studied. Wireless communication technologies suitable for the channels are studied. Additional benefits and services that can be added to a system with the IoT approach are also studied. The effectiveness of such a system is studied with the use of validation framework. Multiple case studies of current and future IoT based ITS along with the challenges in the application is discussed.
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paper_title: Traffic congestion detection in large-scale scenarios using vehicle-to-vehicle communications

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Cooperative vehicular systems are currently being investigated to design innovative ITS (Intelligent Transportation Systems) solutions for road traffic management and safety. Through the wireless exchange of information between vehicles, and between vehicles and infrastructure nodes, cooperative systems can support novel decentralized strategies for ubiquitous and more cost-attractive traffic monitoring. In this context, this paper presents and evaluates CoTEC (COperative Traffic congestion detECtion), a novel cooperative technique based on Vehicle-to-Vehicle (V2V) communications designed to detect road traffic congestion. CoTEC is evaluated under large-scale highway scenarios using iTETRIS, a unique open source simulation platform created to investigate the impact of cooperative vehicular systems. The obtained results demonstrate CoTEC’s capability to accurately detect and characterize road traffic congestion conditions under different traffic scenarios and V2V penetration rates. In particular, CoTEC results in congestion detection probabilities higher than 90%. These results are obtained without overloading the cooperative communications channel. In fact, CoTEC reduces the communications overhead needed to detect road traffic congestions compared to related techniques by 88%.
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paper_title: Vehicular Fog Computing: A Viewpoint of Vehicles as the Infrastructures

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With the emergence of ever-growing advanced vehicular applications, the challenges to meet the demands from both communication and computation are increasingly prominent. Without powerful communication and computational support, various vehicular applications and services will still stay in the concept phase and cannot be put into practice in the daily life. Thus, solving this problem is of great importance. The existing solutions, such as cellular networks, roadside units (RSUs), and mobile cloud computing, are far from perfect because they highly depend on and bear the cost of additional infrastructure deployment. Given tremendous number of vehicles in urban areas, putting these underutilized vehicular resources into use offers great opportunity and value. Therefore, we conceive the idea of utilizing vehicles as the infrastructures for communication and computation, named vehicular fog computing (VFC), which is an architecture that utilizes a collaborative multitude of end-user clients or near-user edge devices to carry out communication and computation, based on better utilization of individual communication and computational resources of each vehicle. By aggregating abundant resources of individual vehicles, the quality of services and applications can be enhanced greatly. In particular, by discussing four types of scenarios of moving and parked vehicles as the communication and computational infrastructures, we carry on a quantitative analysis of the capacities of VFC. We unveil an interesting relationship among the communication capability, connectivity, and mobility of vehicles, and we also find out the characteristics about the pattern of parking behavior, which benefits from the understanding of utilizing the vehicular resources. Finally, we discuss the challenges and open problems in implementing the proposed VFC system as the infrastructures. Our study provides insights for this novel promising paradigm, as well as research topics about vehicular information infrastructures.
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paper_title: Vehicle as a Mobile Sensor

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Abstract Automotive manufacturers are currently giving much interest to in-vehicle sensors and their relevant ITS applications. The number of in-vehicle sensors is in continuously increasing because of their proved benefits represented in avoided accidents, higher driving efficiencies, and ubiquitous sensing-based services. These benefits are not limited to only the vehicle's driver, but also to other vehicles and third parties. In this paper, we introduce Vehicle as a Mobile Sensor (VaaMS), a concept that shows how sensor-equipped vehicles can be considered a pivotal, mobile resource of sensory data and sensor-related applications/services. In addition, we present a new categorization of in-vehicle sensors along with a discussion of some representative sensors and several supported ITS applications. Besides, we elaborate on some communication technologies that support VaaMS.
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paper_title: Optimization of urban road traffic in Intelligent Transport Systems

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The urban traffic congestions represent a major problem in the vast majority of world metropolis and the Intelligent Transport Systems are created to provide real time control and route guidance for the traffic participants, and to optimize the performance of traffic networks. The updated control and the adaptive control strategies are more and more frequently used due to their potential to reduce the delay in the intersections. New opportunities to control the traffic on street networks have been created due to the fast evolution of communications and computerized processing methods in the last decade. The role of urban traffic optimization is to both increase the efficiency of transport services and their capacity to respond to user requirements. This category of systems include information systems that disseminate information concerning the public transport schedule and the exact time-of-arrival of public transport assets, prices, routes, automatic trip tax collection systems, vehicle location systems for fleet management and safety systems. The paper herein provides an analysis of a group of intersections, starting from the Vasile Milea Boulevard, up to its intersection with the 13 Septembrie Road, while trying to determine the optimal maximum green time for each and every intersection, in order to reduce the waiting time at the semaphores, to improve the level of service and to avoid semaphore waiting queues.
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paper_title: Internet of vehicles: Cloud and fog computing approaches

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Some of the mobility challenges in the cities can be approached with the Internet of Things, Cloud Computing, and Fog Computing. In this work we consider the city as a system of systems and focus on the interaction of those entities with the mobility systems. A key element that allows this interaction is a vehicle on-board unit. In this work we address the interconnection, enabling technologies, interoperability, scalability, geo-distribution and time constrains aspects in the architectural design of mobility systems. For this we propose the On-Board Unit architecture and cover three different levels of technification in information and communication technologies. Based on this differentiation we are able to include Cloud and Fog Computing into our proposed designs.
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paper_title: A novel urban traffic management mechanism based on FOG

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An increase of vehicles in a city without an efficient infrastructure of traffic management can cause damages not only financial but also environmental and social. In order to support urban traffic system to relieve the traffic congestion and the damage caused by congestion of vehicles, in this paper, we propose a mechanism for Intelligent Transport Systems named FOg RoutE VEhiculaR (FOREVER), in order to assist the traffic management in Vehicular Networks (VANET). For achieving this, FOREVER will detect and recommend an alternative route for the vehicles to avoid previous congestion. FOREVER is based on FOG computing paradigm that aims of to compute and modify the route of the vehicle to avoid the formation of congestion. Thus, the results show that FOREVER had a reduction about 7.9% of the CO 2 emissions, 8.3% the stop time and 7.6% of the trip time.
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paper_title: Road Oriented Traffic Information System for Vehicular Ad hoc Networks

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Over the last few years, vehicular ad hoc networks (VANETs) have gained popularity for their interesting applications. To make efficient routing decisions, VANET routing protocols require road traffic density information for which they use density estimation schemes. This paper presents a distributed mechanism for road vehicular density estimation that considers multiple road factors, such as road length and junctions. Extensive simulations are carried out to analyze the effectiveness of the proposed technique. Simulation results suggested that, the proposed technique is more accurate compared to the existing technique. Moreover, it facilitate VANET routing protocols to increase packet delivery ratio and reduce end-to-end delay.
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paper_title: V2X-d: A vehicular density estimation system that combines V2V and V2I communications

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Road traffic is experiencing a drastic increase, and vehicular traffic congestion is becoming a major problem, especially in metropolitan environments throughout the world. Additionally, in modern Intelligent Transportation Systems (ITS) communications, the high amount of information that can be generated and processed by vehicles will significantly increase message redundancy, channel contention, and message collisions, thus reducing the efficiency of message dissemination processes. In this work, we present a V2X architecture to estimate traffic density on the road that relies on the advantages of combining V2V and V2I communications. Our proposal uses both the number of beacons received per vehicle (V2V) and per RSU (V2I), as well as the roadmap topology features to estimate the vehicle density. By using our approach, modern Intelligent Transportation Systems will be able to reduce traffic congestion and also to adopt more efficient message dissemination protocols.
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paper_title: V2I Cooperation for Traffic Management with SafeCop

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The Safe Cooperating Cyber-Physical Systems using Wireless Communication (SafeCop) project addresses safety-related issues in cooperating cyber-physical systems. These systems, characterised by wireless communications, multiple stakeholders, and variable operating environments, are called Cooperative Open Cyber-Physical Systems (CO-CPS). CO-CPSs can successfully address several societal challenges -- cooperative vehicles have been shown to reduce fuel consumption as well as the number of accidents. A vehicle-to-infrastructure (V2I) cooperation for the traffic management scenario is therefore considered as a key use cases of SafeCop. In this paper, we outline the V2I traffic management scenario, assess the research goals that arise from it, and provide an overview of the architecture of the demonstrator, as well as a roadmap for its development and evaluation.
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paper_title: Fog Computing: Platform and Applications

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Despite the broad utilization of cloud computing, some applications and services still cannot benefit from this popular computing paradigm due to inherent problems of cloud computing such as unacceptable latency, lack of mobility support and location-awareness. As a result, fog computing, has emerged as a promising infrastructure to provide elastic resources at the edge of network. In this paper, we have discussed current definitions of fog computing and similar concepts, and proposed a more comprehensive definition. We also analyzed the goals and challenges in fog computing platform, and presented platform design with several exemplar applications. We finally implemented and evaluated a prototype fog computing platform.
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paper_title: Vehicular cloud computing: Architectures, applications, and mobility

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Abstract Intelligent transportation systems are designed to provide innovative applications and services relating to traffic management, as well as to facilitate the access to information for other systems and users. The compelling motivation for employing underutilized onboard resources for transportation systems and the advancements in management technology for Cloud computing resources has promoted the concept of Vehicular Clouds. This work gathers and describes the most recent approaches and solutions for Vehicular Clouds, featuring applications, services, and traffic models that can enable Vehicular Cloud in a more dynamic environment. We have considered a large number of applications and services that showed relevance in the scope of the transportation system, benefiting its management, drivers, passengers, and pedestrians. Nevertheless, the high traffic mobility imposes as a significant challenge in implementing a Vehicular Cloud on continually changing physical resources. The dynamics of the environment bring fundamental issues and increase the complexity of building this new type of Cloud. By analyzing the existing traffic models, we found that Vehicular Cloud computing is technologically feasible not just in the static environment, like a parking lot or garage where vehicles are stationary, but also the dynamic scenarios, such as highways or streets where vehicles move.
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paper_title: Road traffic density estimation using microscopic and macroscopic parameters

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In this paper we present a comparative study of two approaches for road traffic density estimation. The first approach uses the microscopic parameters which are extracted using both motion detection and tracking methods from a video sequence, and the second approach uses the macroscopic parameters which are directly estimated by analyzing the global motion in the video scene. The extracted parameters are applied to three classifiers, the K Nearest Neighbor (KNN) classifier, the LVQ classifier and the SVM classifier, in order to classify the road traffic in three categories: light, medium and heavy. The methods are compared based on their robustness to the classification of different road traffic states. The goal of this study is to propose an algorithm for road traffic density estimation with a high precision.
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paper_title: Sensing Traffic Density Combining V2V and V2I Wireless Communications

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Wireless technologies are making the development of new applications and services in vehicular environments possible since they enable mobile communication between vehicles (V2V), as well as communication between vehicles and infrastructure nodes (V2I). Usually, V2V communications are dedicated to the transmission of small messages mainly focused on improving traffic safety. Instead, V2I communications allow users to access the Internet and benefit from higher level applications. The combination of both V2V and V2I, known as V2X communications, can increase the benefits even further, thereby making intelligent transportation systems (ITS) a reality. In this paper, we introduce V2X-d, a novel architecture specially designed to estimate traffic density on the road. In particular, V2X-d exploits the combination of V2V and V2I communications. Our approach is based on the information gathered by sensors (i.e., vehicles and road side units (RSUs)) and the characteristics of the roadmap topology to accurately make an estimation of the instant vehicle density. The combination of both mechanisms improves the accuracy and coverage area of the data gathered, while increasing the robustness and fault tolerance of the overall approach, e.g., using the information offered by V2V communications to provide additional density information in areas where RSUs are scarce or malfunctioning. By using our collaborative sensing scheme, future ITS solutions will be able to establish adequate dissemination protocols or to apply more efficient traffic congestion reduction policies, since they will be aware of the instantaneous density of vehicles.
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paper_title: An Infrastructureless Approach to Estimate Vehicular Density in Urban Environments

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In Vehicular Networks, communication success usually depends on the density of vehicles, since a higher density allows having shorter and more reliable wireless links. Thus, knowing the density of vehicles in a vehicular communications environment is important, as better opportunities for wireless communication can show up. However, vehicle density is highly variable in time and space. This paper deals with the importance of predicting the density of vehicles in vehicular environments to take decisions for enhancing the dissemination of warning messages between vehicles. We propose a novel mechanism to estimate the vehicular density in urban environments. Our mechanism uses as input parameters the number of beacons received per vehicle, and the topological characteristics of the environment where the vehicles are located. Simulation results indicate that, unlike previous proposals solely based on the number of beacons received, our approach is able to accurately estimate the vehicular density, and therefore it could support more efficient dissemination protocols for vehicular environments, as well as improve previously proposed schemes.
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paper_title: Fog Computing: A Taxonomy, Survey and Future Directions

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In recent years, the number of Internet of Things (IoT) devices/sensors has increased to a great extent. To support the computational demand of real-time latency-sensitive applications of largely geo-distributed IoT devices/sensors, a new computing paradigm named “Fog computing” has been introduced. Generally, Fog computing resides closer to the IoT devices/sensors and extends the Cloud-based computing, storage and networking facilities. In this chapter, we comprehensively analyse the challenges in Fogs acting as an intermediate layer between IoT devices/sensors and Cloud datacentres and review the current developments in this field. We present a taxonomy of Fog computing according to the identified challenges and its key features. We also map the existing works to the taxonomy in order to identify current research gaps in the area of Fog computing. Moreover, based on the observations, we propose future directions for research.
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paper_title: FOX: A traffic management system of computer-based vehicles FOG

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Traffic congestion causes drivers' frustration and costs billions of dollars annually in lost time and fuel consumption. In order to overcome such issues, this paper presents a mechanism for Intelligent Transport Systems named FOX (Fast Offset XPath), which aims to detect and manage traffic congestion in Vehicular Ad hoc Networks. FOX is implemented in a FOG computing environment, taking advantage of the aspects inherent to this platform, such as scalability, low latency, the importance of geographical location and network conditions. The focus is to reduce the time to process, reroute and notify vehicles. Simulation results show that the proposed mechanism can reduce the average trip time, CO 2 emissions and fuel consumption. In particular, the average trip time was decreased approximately in 32%, the average fuel consumption in 14% and the stop time in 59%.
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paper_title: A V2I-Based Real-Time Traffic Density Estimation System in Urban Scenarios

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The number of vehicles in our roads is drastically increasing, especially in developing countries. In addition, these vehicles tend to be concentrated in urban areas which present a large population. Since traffic jams have important and mostly negative consequences, such as increasing travel time, fuel consumption, and air pollution, governments are making efforts to alleviate the increasing traffic pressure, being vehicular density one of the main metrics used for assessing the road traffic conditions. However, vehicle density is highly variable in time and space, making it difficult to be estimated accurately. In this paper, we present a solution to estimate the density of vehicles in urban scenarios. Our proposal, that has been specially designed for vehicular networks, allows intelligent transportation systems to continuously estimate vehicular density by accounting for the number of beacons received per road side unit (RSU), and also considering the roadmap topology where the RSUs are located. Using V2I communications, we are able to estimate the traffic density in a certain area, which represents a key parameter to perform efficient traffic redirection, thereby reducing the vehicles' travel time and avoiding traffic jams. Simulation results reveal that, unlike previous proposals, our approach accurately estimates the vehicular density in all the scenarios, presenting an average relative error is of only 3.04 %.
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paper_title: Enabling internet of things for smart homes through fog computing

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Smart grid is the process of applying ICT in order to optimize energy consumption and decrease energy loses. This paper presents a three tier Internet of Thing based hierarchical framework for the smart home, as a reflection to the present lack of intelligent solutions that do not fully use the advantages of Internet of Thing technologies. Our framework aims to extend the smart home to microgrid level, in order to integrate all renewable distributed energy sources from the microgrid and to achieve better energy optimization. As an extension to the traditional data processing, we define fog computing approach for smart home. Through simulation on real smart meter dataset, we showed that fog computing based on predictive filters can reduce the number of transmissions and minimize smart home network traffic.
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paper_title: Vehicular Fog Computing: Architecture, Use Case, and Security and Forensic Challenges

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Vehicular fog computing extends the fog computing paradigm to conventional vehicular networks. This allows us to support more ubiquitous vehicles, achieve better communication efficiency, and address limitations in conventional vehicular networks in terms of latency, location awareness, and real-time response (typically required in smart traffic control, driving safety applications, entertainment services, and other applications). Such requirements are particularly important in adversarial environments (e.g., urban warfare and battlefields in the Internet of Battlefield Things involving military vehicles). However, there is no one widely accepted definition for vehicular fog computing and use cases. Thus, in this article, we formalize the vehicular fog computing architecture and present a typical use case in vehicular fog computing. Then we discuss several key security and forensic challenges and potential solutions.
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paper_title: Real-Time Traffic State Estimation With Connected Vehicles

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A novel framework is developed in this paper, to increase the real-time roadway traffic condition assessment accuracy, which integrates connected vehicle technology (CVT) with artificial intelligence (AI) paradigm forming a CVT-AI method. Traffic density is a major indicator of traffic conditions. In this paper, the traffic operational condition is assessed based on traffic density. A simulated network of Interstate 26 in South Carolina is developed to investigate the effectiveness of the method. The assumption is that the vehicle onboard units will forward the CV generated data to the edge devices (e.g., roadside units) for further processing. CV generated distance headway and number of stops, and speed data are used to estimate traffic density. This paper reveals that, with 20% and greater CV penetration levels, the accuracy of the density information with the AI-aided CVT is a minimum of 85%. Moreover, this paper demonstrates that the integrated CVT-AI method yields a higher accuracy with the increase of CV penetration levels. Level of service (LOS) is the indicator of traffic congestion level on highways and is described with traffic density in terms of passenger car/mile/lane for a specific free flow speed. LOS estimated using the CVT-AI density estimation method is compared with the density estimation algorithm used by the Caltrans Performance Measurement System (PeMS), which relies on the occupancy and flow data collected by the freeway inductive loop detectors. With a 10% or more CV penetration, higher accuracy is achieved using the CVT-AI algorithm compared with the PeMS density estimation algorithm.
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paper_title: An overview of Internet of Vehicles

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The new era of the Internet of Things is driving the evolution of conventional Vehicle Ad-hoc Networks into the Internet of Vehicles (IoV). With the rapid development of computation and communication technologies, IoV promises huge commercial interest and research value, thereby attracting a large number of companies and researchers. This paper proposes an abstract network model of the IoV, discusses the technologies required to create the IoV, presents different applications based on certain currently existing technologies, provides several open research challenges and describes essential future research in the area of IoV.
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paper_title: Vehicular Traffic Density State Estimation Based on Cumulative Road Acoustics

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This paper considers the problem of vehicular traffic density estimation, utilizing the information cues present in the cumulative acoustic signal acquired from a roadside-installed single microphone. This cumulative signal comprises several noise signals such as tire noise, engine noise, engine-idling noise, occasional honks, and air turbulence noise of multiple vehicles. The occurrence and mixture weightings of these noise signals are determined by the prevalent traffic density conditions on the road segment. For instance, under a free-flowing traffic condition, the vehicles typically move with medium to high speeds and thereby produce mainly tire noise and air turbulence noise and less engine-idling noise and honks. For slow-moving congested traffic, the cumulative signal will largely be dominated by engine-idling noise and honks; air turbulence and tire noises will be inconspicuous. Furthermore, these various noise signals have spectral content that are very different from each other and, hence, can be used to discriminate between the different traffic density states that lead to them. Therefore, in this work, we extract the short-term spectral envelope features of the cumulative acoustic signals and model their class-conditional probability distributions, conditioned on one of the three broad traffic density states, i.e., Jammed (0-10 km/h), Medium-Flow (10-40 km/h), and Free-Flow (40 km/h and above) traffic. While these states are coarse measures of the average traffic speed, they nevertheless can provide useful traffic density information in the often-chaotic and nonlane-driven traffic conditions of the developing geographies, where other techniques (magnetic loop detectors) are inapplicable. Based on these learned distributions, we use a Bayes' classifier to classify the acoustic signal segments spanning a duration of 5-30 s, which results in a high classification accuracy of ~95%. Using a discriminative classifier such as a support vector machine (SVM) results in further classification accuracy gains over the Bayes' classifier.
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paper_title: Accurate Vehicle Location System Using RFID, an Internet of Things Approach

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Modern infrastructure, such as dense urban areas and underground tunnels, can effectively block all GPS signals, which implies that effective position triangulation will not be achieved. The main problem that is addressed in this project is the design and implementation of an accurate vehicle location system using radio-frequency identification (RFID) technology in combination with GPS and the Global system for Mobile communication (GSM) technology, in order to provide a solution to the limitation discussed above. In essence, autonomous vehicle tracking will be facilitated with the use of RFID technology where GPS signals are non-existent. The design of the system and the results are reflected in this paper. An extensive literature study was done on the field known as the Internet of Things, as well as various topics that covered the integration of independent technology in order to address a specific challenge. The proposed system is then designed and implemented. An RFID transponder was successfully designed and a read range of approximately 31 cm was obtained in the low frequency communication range (125 kHz to 134 kHz). The proposed system was designed, implemented, and field tested and it was found that a vehicle could be accurately located and tracked. It is also found that the antenna size of both the RFID reader unit and RFID transponder plays a critical role in the maximum communication range that can be achieved.
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paper_title: Vehicle as a Mobile Sensor

paper_content:

Abstract Automotive manufacturers are currently giving much interest to in-vehicle sensors and their relevant ITS applications. The number of in-vehicle sensors is in continuously increasing because of their proved benefits represented in avoided accidents, higher driving efficiencies, and ubiquitous sensing-based services. These benefits are not limited to only the vehicle's driver, but also to other vehicles and third parties. In this paper, we introduce Vehicle as a Mobile Sensor (VaaMS), a concept that shows how sensor-equipped vehicles can be considered a pivotal, mobile resource of sensory data and sensor-related applications/services. In addition, we present a new categorization of in-vehicle sensors along with a discussion of some representative sensors and several supported ITS applications. Besides, we elaborate on some communication technologies that support VaaMS.
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paper_title: Toward mobility support for information-centric IoV in smart city using fog computing

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As a meaningful and typical application scenario of Internet of Things (IoT), Internet of Vehicles (IoV) has attracted a lot of attentions to solve the increasingly severe problem of traffic congestion and safety issues in smart city. Information-centric networks (ICN) is a main stream of next generation network because of its content based forwarding strategy and in-network caching properties. Many existing works have been done to introduce ICN in IoV because of IP-based network architecture's maladjustment of such extremely mobile and dynamic IoV environment. In contrast, ICN is able to sustain packet delivery in unreliable and extreme environment. However, the frequent mobility of the vehicles will consume ICN's network resources to incessantly update the Forward Information Base (FIB) which will further affect the aggregation processing. For example, geographic location based name schema aggregation will be affected dramatically by mobility problem. On the other hand, fog computing is an edge computing technology usually integrated with IoT by bringing computation and storage capacity near the underlying networks to provide low-latency and time sensitive services. In this paper, we integrate fog computing into information-centric IoV to provide mobility support by allocating different schema taking account of the data characteristic (e.g., user-shareable data, communication data). Moreover, we use the computation, storage and location-aware capabilities of fog to design a mobility support mechanism for data exchange and communication considering the feature of IoV service (e.g., alarm danger in local, updating traffic information, V2V communication etc.). We evaluate related performances of the proposed mechanism in high mobility environment, compared with original information-centric IoV. The result shows the advantages of a fog computing based information-centric IoV.
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paper_title: Internet of vehicles: Cloud and fog computing approaches

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Some of the mobility challenges in the cities can be approached with the Internet of Things, Cloud Computing, and Fog Computing. In this work we consider the city as a system of systems and focus on the interaction of those entities with the mobility systems. A key element that allows this interaction is a vehicle on-board unit. In this work we address the interconnection, enabling technologies, interoperability, scalability, geo-distribution and time constrains aspects in the architectural design of mobility systems. For this we propose the On-Board Unit architecture and cover three different levels of technification in information and communication technologies. Based on this differentiation we are able to include Cloud and Fog Computing into our proposed designs.
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paper_title: Cloud Computing in VANETs: Architecture, Taxonomy, and Challenges

paper_content:

Cloud Computing in VANETs (CC-V) has been investigated into two major themes of research including Vehicular Cloud Computing (VCC) and Vehicle using Cloud (VuC). VCC is the realization of autonomous cloud among vehicles to share their abundant resources. VuC is the efficient usage of conventional cloud by on-road vehicles via a reliable Internet connection. Recently, number of advancements have been made to address the issues and challenges in VCC and VuC. This paper qualitatively reviews CC-V with the emphasis on layered architecture, network component, taxonomy, and future challenges. Specifically, a four-layered architecture for CC-V is proposed including perception, co-ordination, artificial intelligence and smart application layers. Three network component of CC-V namely, vehicle, connection and computation are explored with their cooperative roles. A taxonomy for CC-V is presented considering major themes of research in the area including design of architecture, data dissemination, security, and applications. Related literature on each theme are critically investigated with comparative assessment of recent advances. Finally, some open research challenges are identified as future issues. The challenges are the outcome of the critical and qualitative assessment of literature on CC-V.
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paper_title: Vehicular cloud computing: Architectures, applications, and mobility

paper_content:

Abstract Intelligent transportation systems are designed to provide innovative applications and services relating to traffic management, as well as to facilitate the access to information for other systems and users. The compelling motivation for employing underutilized onboard resources for transportation systems and the advancements in management technology for Cloud computing resources has promoted the concept of Vehicular Clouds. This work gathers and describes the most recent approaches and solutions for Vehicular Clouds, featuring applications, services, and traffic models that can enable Vehicular Cloud in a more dynamic environment. We have considered a large number of applications and services that showed relevance in the scope of the transportation system, benefiting its management, drivers, passengers, and pedestrians. Nevertheless, the high traffic mobility imposes as a significant challenge in implementing a Vehicular Cloud on continually changing physical resources. The dynamics of the environment bring fundamental issues and increase the complexity of building this new type of Cloud. By analyzing the existing traffic models, we found that Vehicular Cloud computing is technologically feasible not just in the static environment, like a parking lot or garage where vehicles are stationary, but also the dynamic scenarios, such as highways or streets where vehicles move.
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paper_title: ThinGs In a Fog: System Illustration with Connected Vehicles

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This paper presents ThinGs In a Fog (TGIF)- a system designed to support interdisciplinary research that fall under the broad context of the Internet of Things. The framework is based on an Edge Computing system design that distributes application processing to system compute nodes leveraging geographic compute location diversity of a Cloud-to-the-edge to support machine-to- machine interactions that potentially have real- time constraints. To provide further insight, we focus on Connected Vehicle as an exemplar application domain. This paper provides a summary of work-to-date, including results from a small prototype of the system deployed at Clemson University. We illustrate the system be presenting work-to-date on the design, implementation and evaluation of a Queue Warning which is an application that has been studied thoroughly by the transportation community. This particular application is nicely suited for illustrating the additional benefits and complexities associated with implementing well understood applications in emerging distributed computing environments expected to be supported by the IoT.
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paper_title: Vehicular Fog Computing: Architecture, Use Case, and Security and Forensic Challenges

paper_content:

Vehicular fog computing extends the fog computing paradigm to conventional vehicular networks. This allows us to support more ubiquitous vehicles, achieve better communication efficiency, and address limitations in conventional vehicular networks in terms of latency, location awareness, and real-time response (typically required in smart traffic control, driving safety applications, entertainment services, and other applications). Such requirements are particularly important in adversarial environments (e.g., urban warfare and battlefields in the Internet of Battlefield Things involving military vehicles). However, there is no one widely accepted definition for vehicular fog computing and use cases. Thus, in this article, we formalize the vehicular fog computing architecture and present a typical use case in vehicular fog computing. Then we discuss several key security and forensic challenges and potential solutions.
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paper_title: Vehicular Cloud Computing

paper_content:

Mobile Cloud Computing is a new field of research that aims to study mobile agents (people, vehicles, robots) as they interact and collaborate to sense the environment, process the data, propagate the results and more generally share resources. Mobile agents collectively operate as Mobile Clouds that enable environment modeling, content discovery, data collection and dissemination and other mobile applications in a way not possible, or not efficient, with conventional Internet Cloud models and mobile computing approaches. In this paper, we discuss design principles and research issues in mobile cloud computing. We then focus on the Mobile Vehicular Cloud and review cloud applications ranging from urban sensing to intelligent transportation.
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paper_title: Design and Implementation of a Wireless OBD II Fleet Management System

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This paper describes the work that has been done in the design and development of a wireless OBD II fleet management system. The system aims to measure speed, distance and fuel consumption of vehicles for tracking and analysis purposes. An OBD II reader is designed to measure speed and mass air flow, from which distance and fuel consumption are also computed. This data is then transmitted via WiFi to a remote server. The system also implements GPS tracking to determine the location of the vehicle. A database management system is implemented at the remote server for the storage and management of transmitted data and a graphical user interface (GUI) is developed for analysing the transmitted data . Various qualification tests are conducted to verify the functionality of the system. The results demonstrate that the system is capable of reading the various parameters, and can successfully process, transmit and display the readings.
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paper_title: Vehicular Fog Computing: A Viewpoint of Vehicles as the Infrastructures

paper_content:

With the emergence of ever-growing advanced vehicular applications, the challenges to meet the demands from both communication and computation are increasingly prominent. Without powerful communication and computational support, various vehicular applications and services will still stay in the concept phase and cannot be put into practice in the daily life. Thus, solving this problem is of great importance. The existing solutions, such as cellular networks, roadside units (RSUs), and mobile cloud computing, are far from perfect because they highly depend on and bear the cost of additional infrastructure deployment. Given tremendous number of vehicles in urban areas, putting these underutilized vehicular resources into use offers great opportunity and value. Therefore, we conceive the idea of utilizing vehicles as the infrastructures for communication and computation, named vehicular fog computing (VFC), which is an architecture that utilizes a collaborative multitude of end-user clients or near-user edge devices to carry out communication and computation, based on better utilization of individual communication and computational resources of each vehicle. By aggregating abundant resources of individual vehicles, the quality of services and applications can be enhanced greatly. In particular, by discussing four types of scenarios of moving and parked vehicles as the communication and computational infrastructures, we carry on a quantitative analysis of the capacities of VFC. We unveil an interesting relationship among the communication capability, connectivity, and mobility of vehicles, and we also find out the characteristics about the pattern of parking behavior, which benefits from the understanding of utilizing the vehicular resources. Finally, we discuss the challenges and open problems in implementing the proposed VFC system as the infrastructures. Our study provides insights for this novel promising paradigm, as well as research topics about vehicular information infrastructures.
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paper_title: Cloud Computing in VANETs: Architecture, Taxonomy, and Challenges

paper_content:

Cloud Computing in VANETs (CC-V) has been investigated into two major themes of research including Vehicular Cloud Computing (VCC) and Vehicle using Cloud (VuC). VCC is the realization of autonomous cloud among vehicles to share their abundant resources. VuC is the efficient usage of conventional cloud by on-road vehicles via a reliable Internet connection. Recently, number of advancements have been made to address the issues and challenges in VCC and VuC. This paper qualitatively reviews CC-V with the emphasis on layered architecture, network component, taxonomy, and future challenges. Specifically, a four-layered architecture for CC-V is proposed including perception, co-ordination, artificial intelligence and smart application layers. Three network component of CC-V namely, vehicle, connection and computation are explored with their cooperative roles. A taxonomy for CC-V is presented considering major themes of research in the area including design of architecture, data dissemination, security, and applications. Related literature on each theme are critically investigated with comparative assessment of recent advances. Finally, some open research challenges are identified as future issues. The challenges are the outcome of the critical and qualitative assessment of literature on CC-V.
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paper_title: Vehicular cloud computing: Architectures, applications, and mobility

paper_content:

Abstract Intelligent transportation systems are designed to provide innovative applications and services relating to traffic management, as well as to facilitate the access to information for other systems and users. The compelling motivation for employing underutilized onboard resources for transportation systems and the advancements in management technology for Cloud computing resources has promoted the concept of Vehicular Clouds. This work gathers and describes the most recent approaches and solutions for Vehicular Clouds, featuring applications, services, and traffic models that can enable Vehicular Cloud in a more dynamic environment. We have considered a large number of applications and services that showed relevance in the scope of the transportation system, benefiting its management, drivers, passengers, and pedestrians. Nevertheless, the high traffic mobility imposes as a significant challenge in implementing a Vehicular Cloud on continually changing physical resources. The dynamics of the environment bring fundamental issues and increase the complexity of building this new type of Cloud. By analyzing the existing traffic models, we found that Vehicular Cloud computing is technologically feasible not just in the static environment, like a parking lot or garage where vehicles are stationary, but also the dynamic scenarios, such as highways or streets where vehicles move.
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paper_title: Vehicular Fog Computing: A Viewpoint of Vehicles as the Infrastructures

paper_content:

With the emergence of ever-growing advanced vehicular applications, the challenges to meet the demands from both communication and computation are increasingly prominent. Without powerful communication and computational support, various vehicular applications and services will still stay in the concept phase and cannot be put into practice in the daily life. Thus, solving this problem is of great importance. The existing solutions, such as cellular networks, roadside units (RSUs), and mobile cloud computing, are far from perfect because they highly depend on and bear the cost of additional infrastructure deployment. Given tremendous number of vehicles in urban areas, putting these underutilized vehicular resources into use offers great opportunity and value. Therefore, we conceive the idea of utilizing vehicles as the infrastructures for communication and computation, named vehicular fog computing (VFC), which is an architecture that utilizes a collaborative multitude of end-user clients or near-user edge devices to carry out communication and computation, based on better utilization of individual communication and computational resources of each vehicle. By aggregating abundant resources of individual vehicles, the quality of services and applications can be enhanced greatly. In particular, by discussing four types of scenarios of moving and parked vehicles as the communication and computational infrastructures, we carry on a quantitative analysis of the capacities of VFC. We unveil an interesting relationship among the communication capability, connectivity, and mobility of vehicles, and we also find out the characteristics about the pattern of parking behavior, which benefits from the understanding of utilizing the vehicular resources. Finally, we discuss the challenges and open problems in implementing the proposed VFC system as the infrastructures. Our study provides insights for this novel promising paradigm, as well as research topics about vehicular information infrastructures.
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paper_title: Fog Computing: Platform and Applications

paper_content:

Despite the broad utilization of cloud computing, some applications and services still cannot benefit from this popular computing paradigm due to inherent problems of cloud computing such as unacceptable latency, lack of mobility support and location-awareness. As a result, fog computing, has emerged as a promising infrastructure to provide elastic resources at the edge of network. In this paper, we have discussed current definitions of fog computing and similar concepts, and proposed a more comprehensive definition. We also analyzed the goals and challenges in fog computing platform, and presented platform design with several exemplar applications. We finally implemented and evaluated a prototype fog computing platform.
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paper_title: Vehicular cloud computing: Architectures, applications, and mobility

paper_content:

Abstract Intelligent transportation systems are designed to provide innovative applications and services relating to traffic management, as well as to facilitate the access to information for other systems and users. The compelling motivation for employing underutilized onboard resources for transportation systems and the advancements in management technology for Cloud computing resources has promoted the concept of Vehicular Clouds. This work gathers and describes the most recent approaches and solutions for Vehicular Clouds, featuring applications, services, and traffic models that can enable Vehicular Cloud in a more dynamic environment. We have considered a large number of applications and services that showed relevance in the scope of the transportation system, benefiting its management, drivers, passengers, and pedestrians. Nevertheless, the high traffic mobility imposes as a significant challenge in implementing a Vehicular Cloud on continually changing physical resources. The dynamics of the environment bring fundamental issues and increase the complexity of building this new type of Cloud. By analyzing the existing traffic models, we found that Vehicular Cloud computing is technologically feasible not just in the static environment, like a parking lot or garage where vehicles are stationary, but also the dynamic scenarios, such as highways or streets where vehicles move.
---
paper_title: Fog Computing: A Taxonomy, Survey and Future Directions

paper_content:

In recent years, the number of Internet of Things (IoT) devices/sensors has increased to a great extent. To support the computational demand of real-time latency-sensitive applications of largely geo-distributed IoT devices/sensors, a new computing paradigm named “Fog computing” has been introduced. Generally, Fog computing resides closer to the IoT devices/sensors and extends the Cloud-based computing, storage and networking facilities. In this chapter, we comprehensively analyse the challenges in Fogs acting as an intermediate layer between IoT devices/sensors and Cloud datacentres and review the current developments in this field. We present a taxonomy of Fog computing according to the identified challenges and its key features. We also map the existing works to the taxonomy in order to identify current research gaps in the area of Fog computing. Moreover, based on the observations, we propose future directions for research.
---
paper_title: Vehicular Fog Computing: A Viewpoint of Vehicles as the Infrastructures

paper_content:

With the emergence of ever-growing advanced vehicular applications, the challenges to meet the demands from both communication and computation are increasingly prominent. Without powerful communication and computational support, various vehicular applications and services will still stay in the concept phase and cannot be put into practice in the daily life. Thus, solving this problem is of great importance. The existing solutions, such as cellular networks, roadside units (RSUs), and mobile cloud computing, are far from perfect because they highly depend on and bear the cost of additional infrastructure deployment. Given tremendous number of vehicles in urban areas, putting these underutilized vehicular resources into use offers great opportunity and value. Therefore, we conceive the idea of utilizing vehicles as the infrastructures for communication and computation, named vehicular fog computing (VFC), which is an architecture that utilizes a collaborative multitude of end-user clients or near-user edge devices to carry out communication and computation, based on better utilization of individual communication and computational resources of each vehicle. By aggregating abundant resources of individual vehicles, the quality of services and applications can be enhanced greatly. In particular, by discussing four types of scenarios of moving and parked vehicles as the communication and computational infrastructures, we carry on a quantitative analysis of the capacities of VFC. We unveil an interesting relationship among the communication capability, connectivity, and mobility of vehicles, and we also find out the characteristics about the pattern of parking behavior, which benefits from the understanding of utilizing the vehicular resources. Finally, we discuss the challenges and open problems in implementing the proposed VFC system as the infrastructures. Our study provides insights for this novel promising paradigm, as well as research topics about vehicular information infrastructures.
---
paper_title: Cloud Computing in VANETs: Architecture, Taxonomy, and Challenges

paper_content:

Cloud Computing in VANETs (CC-V) has been investigated into two major themes of research including Vehicular Cloud Computing (VCC) and Vehicle using Cloud (VuC). VCC is the realization of autonomous cloud among vehicles to share their abundant resources. VuC is the efficient usage of conventional cloud by on-road vehicles via a reliable Internet connection. Recently, number of advancements have been made to address the issues and challenges in VCC and VuC. This paper qualitatively reviews CC-V with the emphasis on layered architecture, network component, taxonomy, and future challenges. Specifically, a four-layered architecture for CC-V is proposed including perception, co-ordination, artificial intelligence and smart application layers. Three network component of CC-V namely, vehicle, connection and computation are explored with their cooperative roles. A taxonomy for CC-V is presented considering major themes of research in the area including design of architecture, data dissemination, security, and applications. Related literature on each theme are critically investigated with comparative assessment of recent advances. Finally, some open research challenges are identified as future issues. The challenges are the outcome of the critical and qualitative assessment of literature on CC-V.
---
paper_title: Vehicular cloud computing: Architectures, applications, and mobility

paper_content:

Abstract Intelligent transportation systems are designed to provide innovative applications and services relating to traffic management, as well as to facilitate the access to information for other systems and users. The compelling motivation for employing underutilized onboard resources for transportation systems and the advancements in management technology for Cloud computing resources has promoted the concept of Vehicular Clouds. This work gathers and describes the most recent approaches and solutions for Vehicular Clouds, featuring applications, services, and traffic models that can enable Vehicular Cloud in a more dynamic environment. We have considered a large number of applications and services that showed relevance in the scope of the transportation system, benefiting its management, drivers, passengers, and pedestrians. Nevertheless, the high traffic mobility imposes as a significant challenge in implementing a Vehicular Cloud on continually changing physical resources. The dynamics of the environment bring fundamental issues and increase the complexity of building this new type of Cloud. By analyzing the existing traffic models, we found that Vehicular Cloud computing is technologically feasible not just in the static environment, like a parking lot or garage where vehicles are stationary, but also the dynamic scenarios, such as highways or streets where vehicles move.
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paper_title: A survey on vehicular cloud computing

paper_content:

Vehicular networking has become a significant research area due to its specific features and applications such as standardization, efficient traffic management, road safety and infotainment. Vehicles are expected to carry relatively more communication systems, on board computing facilities, storage and increased sensing power. Hence, several technologies have been deployed to maintain and promote Intelligent Transportation Systems (ITS). Recently, a number of solutions were proposed to address the challenges and issues of vehicular networks. Vehicular Cloud Computing (VCC) is one of the solutions. VCC is a new hybrid technology that has a remarkable impact on traffic management and road safety by instantly using vehicular resources, such as computing, storage and internet for decision making. This paper presents the state-of-the-art survey of vehicular cloud computing. Moreover, we present a taxonomy for vehicular cloud in which special attention has been devoted to the extensive applications, cloud formations, key management, inter cloud communication systems, and broad aspects of privacy and security issues. Through an extensive review of the literature, we design an architecture for VCC, itemize the properties required in vehicular cloud that support this model. We compare this mechanism with normal Cloud Computing (CC) and discuss open research issues and future directions. By reviewing and analyzing literature, we found that VCC is a technologically feasible and economically viable technological shifting paradigm for converging intelligent vehicular networks towards autonomous traffic, vehicle control and perception systems.
---
paper_title: Vehicular Fog Computing: A Viewpoint of Vehicles as the Infrastructures

paper_content:

With the emergence of ever-growing advanced vehicular applications, the challenges to meet the demands from both communication and computation are increasingly prominent. Without powerful communication and computational support, various vehicular applications and services will still stay in the concept phase and cannot be put into practice in the daily life. Thus, solving this problem is of great importance. The existing solutions, such as cellular networks, roadside units (RSUs), and mobile cloud computing, are far from perfect because they highly depend on and bear the cost of additional infrastructure deployment. Given tremendous number of vehicles in urban areas, putting these underutilized vehicular resources into use offers great opportunity and value. Therefore, we conceive the idea of utilizing vehicles as the infrastructures for communication and computation, named vehicular fog computing (VFC), which is an architecture that utilizes a collaborative multitude of end-user clients or near-user edge devices to carry out communication and computation, based on better utilization of individual communication and computational resources of each vehicle. By aggregating abundant resources of individual vehicles, the quality of services and applications can be enhanced greatly. In particular, by discussing four types of scenarios of moving and parked vehicles as the communication and computational infrastructures, we carry on a quantitative analysis of the capacities of VFC. We unveil an interesting relationship among the communication capability, connectivity, and mobility of vehicles, and we also find out the characteristics about the pattern of parking behavior, which benefits from the understanding of utilizing the vehicular resources. Finally, we discuss the challenges and open problems in implementing the proposed VFC system as the infrastructures. Our study provides insights for this novel promising paradigm, as well as research topics about vehicular information infrastructures.
---
paper_title: Fog of Everything: Energy-Efficient Networked Computing Architectures, Research Challenges, and a Case Study

paper_content:

Fog computing (FC) and Internet of Everything (IoE) are two emerging technological paradigms that, to date, have been considered standing-alone. However, because of their complementary features, we expect that their integration can foster a number of computing and network-intensive pervasive applications under the incoming realm of the future Internet. Motivated by this consideration, the goal of this position paper is fivefold. First, we review the technological attributes and platforms proposed in the current literature for the standing-alone FC and IoE paradigms. Second, by leveraging some use cases as illustrative examples, we point out that the integration of the FC and IoE paradigms may give rise to opportunities for new applications in the realms of the IoE, Smart City, Industry 4.0, and Big Data Streaming, while introducing new open issues. Third, we propose a novel technological paradigm, the Fog of Everything (FoE) paradigm, that integrates FC and IoE and then we detail the main building blocks and services of the corresponding technological platform and protocol stack. Fourth, as a proof-of-concept, we present the simulated energy-delay performance of a small-scale FoE prototype, namely, the V-FoE prototype. Afterward, we compare the obtained performance with the corresponding one of a benchmark technological platform, e.g., the V-D2D one. It exploits only device-to-device links to establish inter-thing “ad hoc” communication. Last, we point out the position of the proposed FoE paradigm over a spectrum of seemingly related recent research projects.
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paper_title: Fog Computing: Platform and Applications

paper_content:

Despite the broad utilization of cloud computing, some applications and services still cannot benefit from this popular computing paradigm due to inherent problems of cloud computing such as unacceptable latency, lack of mobility support and location-awareness. As a result, fog computing, has emerged as a promising infrastructure to provide elastic resources at the edge of network. In this paper, we have discussed current definitions of fog computing and similar concepts, and proposed a more comprehensive definition. We also analyzed the goals and challenges in fog computing platform, and presented platform design with several exemplar applications. We finally implemented and evaluated a prototype fog computing platform.
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paper_title: Internet of Things Framework for Home Care Systems

paper_content:

The increasing average age of the population in most industrialized countries imposes a necessity for developing advanced and practical services using state-of-the-art technologies, dedicated to personal living spaces. In this paper, we introduce a hierarchical distributed approach for home care systems based on a new paradigm known as Internet of Things (IoT). The proposed generic framework is supported by a three-level data management model composed of dew computing, fog computing, and cloud computing for efficient data flow in IoT based home care systems. We examine the proposed model through a real case scenario of an early fire detection system using a distributed fuzzy logic approach. The obtained results prove that such implementation of dew and fog computing provides high accuracy in fire detection IoT systems, while achieving minimum data latency.
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paper_title: Fog Computing: A Taxonomy, Survey and Future Directions

paper_content:

In recent years, the number of Internet of Things (IoT) devices/sensors has increased to a great extent. To support the computational demand of real-time latency-sensitive applications of largely geo-distributed IoT devices/sensors, a new computing paradigm named “Fog computing” has been introduced. Generally, Fog computing resides closer to the IoT devices/sensors and extends the Cloud-based computing, storage and networking facilities. In this chapter, we comprehensively analyse the challenges in Fogs acting as an intermediate layer between IoT devices/sensors and Cloud datacentres and review the current developments in this field. We present a taxonomy of Fog computing according to the identified challenges and its key features. We also map the existing works to the taxonomy in order to identify current research gaps in the area of Fog computing. Moreover, based on the observations, we propose future directions for research.
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paper_title: A Comparison of 802.11a and 802.11p for V-to-I Communication: A Measurement Study

paper_content:

802.11p, also known as WAVE, is a standard protocol intended for future traffic systems in order to support safety and commercial non-safety applications for vehicular communication. 802.11p is modified from 802.11a, and both are based on OFDM. The main difference between 802.11a and 802.11p is that the latter is proposed to use 10 MHz frequency bandwidth (half of bandwidth of 802.11a) in order to make the signal more robust against fading and increase the tolerance for multipath propagation effects of signals in a vehicular environment. In this paper, we investigate the performance difference between 802.11a and 802.11p for Vehicle-to-Infrastructure communication through real-world experiments. We measure contact duration and losses of 802.11p and 802.11a in both LOS and NLOS environments. In addition, we investigate their throughput with different modulations over various distances between OBU and RSU to evaluate the feasibility of using rate adaptation for non-safety V-to-I applications.
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paper_title: Enabling internet of things for smart homes through fog computing

paper_content:

Smart grid is the process of applying ICT in order to optimize energy consumption and decrease energy loses. This paper presents a three tier Internet of Thing based hierarchical framework for the smart home, as a reflection to the present lack of intelligent solutions that do not fully use the advantages of Internet of Thing technologies. Our framework aims to extend the smart home to microgrid level, in order to integrate all renewable distributed energy sources from the microgrid and to achieve better energy optimization. As an extension to the traditional data processing, we define fog computing approach for smart home. Through simulation on real smart meter dataset, we showed that fog computing based on predictive filters can reduce the number of transmissions and minimize smart home network traffic.
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paper_title: Traffic management systems: A classification, review, challenges, and future perspectives:

paper_content:

In cities, where the number of vehicles continuously increases faster than the available traffic infrastructure to support them, congestion is a difficult issue to deal with and it becomes even worse in case of car accidents. This problem affects many aspects of the modern society, including economic development, traffic accidents, increase in greenhouse emissions, time spent, and health damages. In this context, modern societies can rely on traffic management system to minimize traffic congestion and its negative effects. Traffic management systems are composed of a set of application and management tools to improve the overall traffic efficiency and safety of the transportation systems. Furthermore, to overcome such issue, traffic management system gathers information from heterogeneous sources, exploits such information to identify hazards that may potentially degrade the traffic efficiency, and then provides services to control them. With this question in mind, this article presents a classification, ...
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paper_title: Accurate Vehicle Location System Using RFID, an Internet of Things Approach

paper_content:

Modern infrastructure, such as dense urban areas and underground tunnels, can effectively block all GPS signals, which implies that effective position triangulation will not be achieved. The main problem that is addressed in this project is the design and implementation of an accurate vehicle location system using radio-frequency identification (RFID) technology in combination with GPS and the Global system for Mobile communication (GSM) technology, in order to provide a solution to the limitation discussed above. In essence, autonomous vehicle tracking will be facilitated with the use of RFID technology where GPS signals are non-existent. The design of the system and the results are reflected in this paper. An extensive literature study was done on the field known as the Internet of Things, as well as various topics that covered the integration of independent technology in order to address a specific challenge. The proposed system is then designed and implemented. An RFID transponder was successfully designed and a read range of approximately 31 cm was obtained in the low frequency communication range (125 kHz to 134 kHz). The proposed system was designed, implemented, and field tested and it was found that a vehicle could be accurately located and tracked. It is also found that the antenna size of both the RFID reader unit and RFID transponder plays a critical role in the maximum communication range that can be achieved.
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paper_title: Vehicle as a Mobile Sensor

paper_content:

Abstract Automotive manufacturers are currently giving much interest to in-vehicle sensors and their relevant ITS applications. The number of in-vehicle sensors is in continuously increasing because of their proved benefits represented in avoided accidents, higher driving efficiencies, and ubiquitous sensing-based services. These benefits are not limited to only the vehicle's driver, but also to other vehicles and third parties. In this paper, we introduce Vehicle as a Mobile Sensor (VaaMS), a concept that shows how sensor-equipped vehicles can be considered a pivotal, mobile resource of sensory data and sensor-related applications/services. In addition, we present a new categorization of in-vehicle sensors along with a discussion of some representative sensors and several supported ITS applications. Besides, we elaborate on some communication technologies that support VaaMS.
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paper_title: A novel urban traffic management mechanism based on FOG

paper_content:

An increase of vehicles in a city without an efficient infrastructure of traffic management can cause damages not only financial but also environmental and social. In order to support urban traffic system to relieve the traffic congestion and the damage caused by congestion of vehicles, in this paper, we propose a mechanism for Intelligent Transport Systems named FOg RoutE VEhiculaR (FOREVER), in order to assist the traffic management in Vehicular Networks (VANET). For achieving this, FOREVER will detect and recommend an alternative route for the vehicles to avoid previous congestion. FOREVER is based on FOG computing paradigm that aims of to compute and modify the route of the vehicle to avoid the formation of congestion. Thus, the results show that FOREVER had a reduction about 7.9% of the CO 2 emissions, 8.3% the stop time and 7.6% of the trip time.
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paper_title: ThinGs In a Fog: System Illustration with Connected Vehicles

paper_content:

This paper presents ThinGs In a Fog (TGIF)- a system designed to support interdisciplinary research that fall under the broad context of the Internet of Things. The framework is based on an Edge Computing system design that distributes application processing to system compute nodes leveraging geographic compute location diversity of a Cloud-to-the-edge to support machine-to- machine interactions that potentially have real- time constraints. To provide further insight, we focus on Connected Vehicle as an exemplar application domain. This paper provides a summary of work-to-date, including results from a small prototype of the system deployed at Clemson University. We illustrate the system be presenting work-to-date on the design, implementation and evaluation of a Queue Warning which is an application that has been studied thoroughly by the transportation community. This particular application is nicely suited for illustrating the additional benefits and complexities associated with implementing well understood applications in emerging distributed computing environments expected to be supported by the IoT.
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paper_title: Fog Computing: A Taxonomy, Survey and Future Directions

paper_content:

In recent years, the number of Internet of Things (IoT) devices/sensors has increased to a great extent. To support the computational demand of real-time latency-sensitive applications of largely geo-distributed IoT devices/sensors, a new computing paradigm named “Fog computing” has been introduced. Generally, Fog computing resides closer to the IoT devices/sensors and extends the Cloud-based computing, storage and networking facilities. In this chapter, we comprehensively analyse the challenges in Fogs acting as an intermediate layer between IoT devices/sensors and Cloud datacentres and review the current developments in this field. We present a taxonomy of Fog computing according to the identified challenges and its key features. We also map the existing works to the taxonomy in order to identify current research gaps in the area of Fog computing. Moreover, based on the observations, we propose future directions for research.
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paper_title: FOX: A traffic management system of computer-based vehicles FOG

paper_content:

Traffic congestion causes drivers' frustration and costs billions of dollars annually in lost time and fuel consumption. In order to overcome such issues, this paper presents a mechanism for Intelligent Transport Systems named FOX (Fast Offset XPath), which aims to detect and manage traffic congestion in Vehicular Ad hoc Networks. FOX is implemented in a FOG computing environment, taking advantage of the aspects inherent to this platform, such as scalability, low latency, the importance of geographical location and network conditions. The focus is to reduce the time to process, reroute and notify vehicles. Simulation results show that the proposed mechanism can reduce the average trip time, CO 2 emissions and fuel consumption. In particular, the average trip time was decreased approximately in 32%, the average fuel consumption in 14% and the stop time in 59%.
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paper_title: Vehicular Fog Computing: Architecture, Use Case, and Security and Forensic Challenges

paper_content:

Vehicular fog computing extends the fog computing paradigm to conventional vehicular networks. This allows us to support more ubiquitous vehicles, achieve better communication efficiency, and address limitations in conventional vehicular networks in terms of latency, location awareness, and real-time response (typically required in smart traffic control, driving safety applications, entertainment services, and other applications). Such requirements are particularly important in adversarial environments (e.g., urban warfare and battlefields in the Internet of Battlefield Things involving military vehicles). However, there is no one widely accepted definition for vehicular fog computing and use cases. Thus, in this article, we formalize the vehicular fog computing architecture and present a typical use case in vehicular fog computing. Then we discuss several key security and forensic challenges and potential solutions.
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paper_title: A Pub/Sub-Based Fog Computing Architecture for Internet-of-Vehicles

paper_content:

Fog computing is a promising paradigm in terms of extending cloud computing to an edge network. In a broad sense, fog computing in Internet-of-Vehicles(IoV) provides low-latency services since fog nodes are closely located with moving cars and are locally distributed. In this paper, we propose a fog computing architecture based on a publish/subscribe model. After that, we describe a traffic congestion control scenario using a smart traffic light system which operates on top of the proposed architecture. Furthermore, we propose an upper-level domain ontology in order to enhance the expressivity of knowledge and describe a variety of semantic properties which interlink spatial information in IoV. Finally, we present an active rule where supports the exchange of event-driven messages between publishing and subscribing fog nodes.
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paper_title: Design and Implementation of a Wireless OBD II Fleet Management System

paper_content:

This paper describes the work that has been done in the design and development of a wireless OBD II fleet management system. The system aims to measure speed, distance and fuel consumption of vehicles for tracking and analysis purposes. An OBD II reader is designed to measure speed and mass air flow, from which distance and fuel consumption are also computed. This data is then transmitted via WiFi to a remote server. The system also implements GPS tracking to determine the location of the vehicle. A database management system is implemented at the remote server for the storage and management of transmitted data and a graphical user interface (GUI) is developed for analysing the transmitted data . Various qualification tests are conducted to verify the functionality of the system. The results demonstrate that the system is capable of reading the various parameters, and can successfully process, transmit and display the readings.
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paper_title: Energy Dissipation Based Longitudinal and Lateral Coupling Control for Intelligent Vehicles

paper_content:

This paper proposes a combined longitudinal and lateral control approach for an intelligent vehicle system based on energy dissipation. The vehicle system dynamics resembles a series of mass/spring/damper systems that are dissipative, i.e., the energy of the system decays to zero eventually. Thus, the nonlinear-optimal longitudinal and lateral coupling control problem of the intelligent vehicle system is transformed into a dissipative control design based on an energy storage function. To satisfy the g-performance, with respect to the quadratic supply rate, the storage function is developed by using a back-stepping based Lyapunov method and a step-by-step improvement of performance bounds. A dissipative feedback control law is formulated by successive approximation based on the stepby-step reduction of the value of g. The results of the adaptive vehicle control simulations and test-bed experiments are provided and verified by the respective comparison of energy consumption on different values of g and speed adaption under different road geometries.
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paper_title: Cloud-enabled vehicular congestion estimation: An ITS application

paper_content:

Increased traffic and hence congestion is a major problem in cities or urban areas. To mitigate this problem of congestion a real-time traffic density estimation model is essential. This research proposes one such model for estimating the traffic congestion level with the help of Vehicular Ad-hoc Network (VANET) and Cloud Computing. In this work, a novel architecture and algorithm has been proposed to estimate the density of vehicles on the road and the average speed. The fuzzy algorithm is then used to get the level of congestion. The algorithms are simulated using Network Simulator 3 (NS3) and Simulation of Urban Mobility (SUMO) to estimate the congestion level in an area of a city. The model proposed is deployed on Cloud and can be made available as Software as a Service (SaaS) in future.
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paper_title: Simulation-Based Traffic Management System for Connected and Autonomous Vehicles

paper_content:

The paper presents an idea for a simulation-based traffic management system, which may be especially successful in the era of connected and autonomous vehicles (CAVs). The most important aspect of the system is its ability to evaluate traffic conditions for different traffic control strategies (e.g., different traffic signal settings, different route assignments) using fast traffic simulations and neural networks. It also employs metaheuristics (e.g., genetic algorithms) to find (sub)optimal traffic control strategies. Results of initial experiments show that building such traffic management system might be technically feasible and it may be especially successful in the era of CAVs, for which it may be possible to collect required traffic data and make accurate traffic predictions.
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paper_title: Road traffic density estimation using microscopic and macroscopic parameters

paper_content:

In this paper we present a comparative study of two approaches for road traffic density estimation. The first approach uses the microscopic parameters which are extracted using both motion detection and tracking methods from a video sequence, and the second approach uses the macroscopic parameters which are directly estimated by analyzing the global motion in the video scene. The extracted parameters are applied to three classifiers, the K Nearest Neighbor (KNN) classifier, the LVQ classifier and the SVM classifier, in order to classify the road traffic in three categories: light, medium and heavy. The methods are compared based on their robustness to the classification of different road traffic states. The goal of this study is to propose an algorithm for road traffic density estimation with a high precision.
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paper_title: An efficient detection scheme for urban traffic condition using volunteer probes

paper_content:

In urban areas, traffic congestion has become a common phenomenon resulting in a great waste of time and fuel. Thus, the real-time detection for road condition becomes a significant challenge for both scientists and engineers. Recently, the technology of vehicular ad-hoc networks (VANETs) has been utilized to alleviate this problem, and becomes an important component of the intelligent transportation system (ITS). In this paper, we propose a novel detection scheme for urban traffic condition only by volunteer probes. The travel time for detected road segment is used to evaluate the traffic condition. To reduce the influence of individuals, a T Window Algorithm (TWA) is designed for calculating the average travel time of probes. For performance optimization, three kinds of Adaptive T Window Algorithm (ATWA) are put forward to resize window according to the probe density. Besides, on analysis of travel time composition, a congestion line is built to identify the traffic status of road segment. Finally, all the approaches are validated in a realistic scenario. The simulation results show that our scheme can detect urban traffic condition effectively.
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paper_title: Urban Area Congestion Detection and Propagation Using Histogram Model

paper_content:

Detecting congestion in urban areas is critical and creates a myriad of complications. Intelligent Transportation Systems (ITS), which are trending in recent years, are used by researchers to engage problems related to congestion and transportation. However, due to the open access in urban area structures, it is less feasible to handle rife data that is generated from vehicles and infrastructure. On the grounds, ITS demands a reliable methodology that uses the data's effectively to detect the congestion. In this paper, we present a novel congestion estimation model for urban areas that leads to predict the congestion propagation. It uses a histogram-based model on a window time basis to make the data transfer substantially minimum and keep the system robust. Due to its simplicity, it can be practically implemented in real time for any nature of roadways. Simulation results, with different scenarios, show that the proposed model is detecting the congestion estimation effectively and leads to predict the congestion propagation for the near future.
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paper_title: Road Oriented Traffic Information System for Vehicular Ad hoc Networks

paper_content:

Over the last few years, vehicular ad hoc networks (VANETs) have gained popularity for their interesting applications. To make efficient routing decisions, VANET routing protocols require road traffic density information for which they use density estimation schemes. This paper presents a distributed mechanism for road vehicular density estimation that considers multiple road factors, such as road length and junctions. Extensive simulations are carried out to analyze the effectiveness of the proposed technique. Simulation results suggested that, the proposed technique is more accurate compared to the existing technique. Moreover, it facilitate VANET routing protocols to increase packet delivery ratio and reduce end-to-end delay.
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paper_title: An infrastructure based congestion detection and avoidance scheme for VANETs

paper_content:

In urban areas, traffic congestion is a challenging problem which creates many complications to travelers. Unattended congestion in roadways will cause long waiting times and affects individual economy. It also creates environmental issues and affects personal health. In recent years, the development of Vehicular Ad-hoc Network (VANET) provides a promising solutions for handling the congestion in the roadways. In this paper, we adapt a VANET networking model and propose a novel congestion detection and avoidance scheme for urban areas. A light weight histogram model is utilized to compute the congestion for every lane using an infrastructure based system. By computing the probability density function for every lane, the proposed model predicts congestion in advance and the re-routing strategy is initiated on time. For optimizing the re-routing strategy, two different kinds of congestion avoidance scheme are proposed based on static and dynamic modelling. Various scenarios are created in the microscopic simulation environment and the effectiveness of the proposed algorithm is analyzed by measuring the average travel time. The simulation results show that the proposed model detects congestion in priori and initiates reroute strategy effectively.
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paper_title: Sensing Traffic Density Combining V2V and V2I Wireless Communications

paper_content:

Wireless technologies are making the development of new applications and services in vehicular environments possible since they enable mobile communication between vehicles (V2V), as well as communication between vehicles and infrastructure nodes (V2I). Usually, V2V communications are dedicated to the transmission of small messages mainly focused on improving traffic safety. Instead, V2I communications allow users to access the Internet and benefit from higher level applications. The combination of both V2V and V2I, known as V2X communications, can increase the benefits even further, thereby making intelligent transportation systems (ITS) a reality. In this paper, we introduce V2X-d, a novel architecture specially designed to estimate traffic density on the road. In particular, V2X-d exploits the combination of V2V and V2I communications. Our approach is based on the information gathered by sensors (i.e., vehicles and road side units (RSUs)) and the characteristics of the roadmap topology to accurately make an estimation of the instant vehicle density. The combination of both mechanisms improves the accuracy and coverage area of the data gathered, while increasing the robustness and fault tolerance of the overall approach, e.g., using the information offered by V2V communications to provide additional density information in areas where RSUs are scarce or malfunctioning. By using our collaborative sensing scheme, future ITS solutions will be able to establish adequate dissemination protocols or to apply more efficient traffic congestion reduction policies, since they will be aware of the instantaneous density of vehicles.
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paper_title: Local Density Estimation and Dynamic Transmission-Range Assignment in Vehicular Ad Hoc Networks

paper_content:

Vehicular ad hoc networks have several characteristics that distinguish them from other ad hoc networks. Among those is the rapid change in topology due to traffic jams, which also disturbs the homogeneous distribution of vehicles on the road. For this reason, a dynamic transmission range is more effective in maintaining the connectivity while minimizing the adverse effects of a high transmission power. This paper proposes a scheme that allows vehicles to estimate the local density and distinguish between the free-flow and the congested traffic phases. The density estimate is used to develop a dynamic transmission-range-assignment (DTRA) algorithm that sets a vehicle transmission range dynamically according to the local traffic conditions. Simulations of several road configurations validate the quality of the local density estimation and show that the DTRA algorithm is successful in maintaining the connectivity in highly dynamic networks.
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paper_title: Sensing Traffic Density Combining V2V and V2I Wireless Communications

paper_content:

Wireless technologies are making the development of new applications and services in vehicular environments possible since they enable mobile communication between vehicles (V2V), as well as communication between vehicles and infrastructure nodes (V2I). Usually, V2V communications are dedicated to the transmission of small messages mainly focused on improving traffic safety. Instead, V2I communications allow users to access the Internet and benefit from higher level applications. The combination of both V2V and V2I, known as V2X communications, can increase the benefits even further, thereby making intelligent transportation systems (ITS) a reality. In this paper, we introduce V2X-d, a novel architecture specially designed to estimate traffic density on the road. In particular, V2X-d exploits the combination of V2V and V2I communications. Our approach is based on the information gathered by sensors (i.e., vehicles and road side units (RSUs)) and the characteristics of the roadmap topology to accurately make an estimation of the instant vehicle density. The combination of both mechanisms improves the accuracy and coverage area of the data gathered, while increasing the robustness and fault tolerance of the overall approach, e.g., using the information offered by V2V communications to provide additional density information in areas where RSUs are scarce or malfunctioning. By using our collaborative sensing scheme, future ITS solutions will be able to establish adequate dissemination protocols or to apply more efficient traffic congestion reduction policies, since they will be aware of the instantaneous density of vehicles.
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paper_title: Short paper: Distributed vehicular traffic congestion detection algorithm for urban environments

paper_content:

Vehicular traffic congestion is a well-known economic and social problem generating significant costs and safety challenges, and increasing pollution in the cities. Current intelligent transport systems and vehicular networking technologies rely heavily on the supporting network infrastructure which is still not widely available. This paper contributes towards the development of distributed and cooperative vehicular traffic congestion detection by proposing a new vehicle-to-vehicle (V2V) congestion detection algorithm based on the IEEE 802.11p standard. The new algorithm allows vehicles to be self-aware of the traffic in the street, performing congestion detection based on speed monitoring and cooperation with the surrounding vehicles. Cooperation is achieved using adaptive single-hop broadcasting which depends on the level of congestion. The paper presents the congestion detection algorithm and the cooperative communication in detail, and presents performance evaluation using large-scale simulation in Veins framework based on OMNeT++ network simulator and SUMO vehicular mobility simulator. Results show that precise congestion detection and quantification can be achieved using a significantly decreased number of exchanged packets.
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paper_title: Detecting Urban Road Condition and Disseminating Traffic Information by VANETs

paper_content:

In urban areas, traffic congestion has become a serious phenomenon resulting in a great waste of time and fuel. How to detect road condition and disseminate traffic information effectively has become a significant challenge. Recently, the technology of vehicular ad-hoc networks (VANETs) has been utilized to deal with this problem, and become an important component of the intelligent transportation system (ITS). As two basic paradigms, both vehicle-to-vehicle (V2V) and vehicle to-RSU (V2R) communications have their own advantages and disadvantages. In this paper, we propose two novel approaches based on V2V and V2R communications respectively to explore a more suitable candidate for the traffic management. The travel time of vehicles is selected as the common indicator to evaluate the traffic state of route. On the basis of it, a routing algorithm is designed to provide a reasonable route for drivers. Finally, our two approaches are validated in a realistic scenario by Veins. The simulation results show that the performance of V2R scheme is restricted by the limited transmission range of RSU, while the V2V scheme is more appropriate for traffic information dissemination if the broadcast storm is well solved.
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paper_title: An Infrastructureless Approach to Estimate Vehicular Density in Urban Environments

paper_content:

In Vehicular Networks, communication success usually depends on the density of vehicles, since a higher density allows having shorter and more reliable wireless links. Thus, knowing the density of vehicles in a vehicular communications environment is important, as better opportunities for wireless communication can show up. However, vehicle density is highly variable in time and space. This paper deals with the importance of predicting the density of vehicles in vehicular environments to take decisions for enhancing the dissemination of warning messages between vehicles. We propose a novel mechanism to estimate the vehicular density in urban environments. Our mechanism uses as input parameters the number of beacons received per vehicle, and the topological characteristics of the environment where the vehicles are located. Simulation results indicate that, unlike previous proposals solely based on the number of beacons received, our approach is able to accurately estimate the vehicular density, and therefore it could support more efficient dissemination protocols for vehicular environments, as well as improve previously proposed schemes.
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paper_title: A V2I-Based Real-Time Traffic Density Estimation System in Urban Scenarios

paper_content:

The number of vehicles in our roads is drastically increasing, especially in developing countries. In addition, these vehicles tend to be concentrated in urban areas which present a large population. Since traffic jams have important and mostly negative consequences, such as increasing travel time, fuel consumption, and air pollution, governments are making efforts to alleviate the increasing traffic pressure, being vehicular density one of the main metrics used for assessing the road traffic conditions. However, vehicle density is highly variable in time and space, making it difficult to be estimated accurately. In this paper, we present a solution to estimate the density of vehicles in urban scenarios. Our proposal, that has been specially designed for vehicular networks, allows intelligent transportation systems to continuously estimate vehicular density by accounting for the number of beacons received per road side unit (RSU), and also considering the roadmap topology where the RSUs are located. Using V2I communications, we are able to estimate the traffic density in a certain area, which represents a key parameter to perform efficient traffic redirection, thereby reducing the vehicles' travel time and avoiding traffic jams. Simulation results reveal that, unlike previous proposals, our approach accurately estimates the vehicular density in all the scenarios, presenting an average relative error is of only 3.04 %.
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paper_title: TrackT : accurate tracking of RFID tags with mm-level accuracy using first-order Taylor series approximation

paper_content:

Abstract Radio Frequency Identification (RFID) technology is widely used to achieve indoor object tracking and positioning. Currently, many methods need to deploy a large number of reference tags beforehand and some are limited by antennas’ spacing. Further, the signal propagation along Non-Line of Sight introduces multipath effects which will challenge the accuracy of RFID localization system. In this work, we propose a method based on measured phase to track mobile RFID tags with millimeter level (mm-level) accuracy. We first partition the surveillance region into square grids at mm-level and suppose that there is a virtual tag as the same as the tracked one in each grid. On this basis, for the case where the tags move along a known track with constant speed, we only need to locate the tag's initial position. We leverage phase periodicity to obtain some candidates and then eliminate position ambiguity by double difference true phase. And for the case where the tag's moving track is unknown to the system, we adopt a first-order Taylor series expansion to calculate the relative displacements of the tracked tag and then locate the initial position as the same process as tracking the known trajectory. In our experiment, our solution can achieve a mean error distance of 0.26 cm and 0.55 cm for known and unknown movement tracks respectively.
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paper_title: Fog of Everything: Energy-Efficient Networked Computing Architectures, Research Challenges, and a Case Study

paper_content:

Fog computing (FC) and Internet of Everything (IoE) are two emerging technological paradigms that, to date, have been considered standing-alone. However, because of their complementary features, we expect that their integration can foster a number of computing and network-intensive pervasive applications under the incoming realm of the future Internet. Motivated by this consideration, the goal of this position paper is fivefold. First, we review the technological attributes and platforms proposed in the current literature for the standing-alone FC and IoE paradigms. Second, by leveraging some use cases as illustrative examples, we point out that the integration of the FC and IoE paradigms may give rise to opportunities for new applications in the realms of the IoE, Smart City, Industry 4.0, and Big Data Streaming, while introducing new open issues. Third, we propose a novel technological paradigm, the Fog of Everything (FoE) paradigm, that integrates FC and IoE and then we detail the main building blocks and services of the corresponding technological platform and protocol stack. Fourth, as a proof-of-concept, we present the simulated energy-delay performance of a small-scale FoE prototype, namely, the V-FoE prototype. Afterward, we compare the obtained performance with the corresponding one of a benchmark technological platform, e.g., the V-D2D one. It exploits only device-to-device links to establish inter-thing “ad hoc” communication. Last, we point out the position of the proposed FoE paradigm over a spectrum of seemingly related recent research projects.
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paper_title: Hierarchical Fuzzy Logic-Based Variable Structure Control for Vehicles Platooning

paper_content:

This paper proposes a variable structure control approach for vehicles platooning based on a hierarchical fuzzy logic. The leader-follower vehicle dynamics with model uncertainties is discussed from the viewpoint of a consensus problem. A practical two-layer fuzzy control for the platooning is designed by employing two common spacing policies to ensure system robustness in different scenarios. The two policies, i.e., constant distance and constant time headway, utilize the predecessor-successor information flow from the immediate predecessor and follower other than controlled vehicles. The first layer of the fuzzy system combines spacing control with velocity-acceleration control to achieve a rapid tracking for the desired control commands, and the second layer combines the sliding mode control to adaptively compensate for reducing the state errors caused by parameter uncertainties and disturbances. Shift between different controller parameters is based on performance boundaries to guarantee the stability of individual vehicle and platooning for arbitrary initial spacing and velocity errors. These performance boundaries can be determined by using a Lyapunov method with exponential stability. Simulation of a ten-vehicle large platooning with two spacing policies shows that the control performance of the newly proposed method is effective and promising.
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paper_title: Sensing Traffic Density Combining V2V and V2I Wireless Communications

paper_content:

Wireless technologies are making the development of new applications and services in vehicular environments possible since they enable mobile communication between vehicles (V2V), as well as communication between vehicles and infrastructure nodes (V2I). Usually, V2V communications are dedicated to the transmission of small messages mainly focused on improving traffic safety. Instead, V2I communications allow users to access the Internet and benefit from higher level applications. The combination of both V2V and V2I, known as V2X communications, can increase the benefits even further, thereby making intelligent transportation systems (ITS) a reality. In this paper, we introduce V2X-d, a novel architecture specially designed to estimate traffic density on the road. In particular, V2X-d exploits the combination of V2V and V2I communications. Our approach is based on the information gathered by sensors (i.e., vehicles and road side units (RSUs)) and the characteristics of the roadmap topology to accurately make an estimation of the instant vehicle density. The combination of both mechanisms improves the accuracy and coverage area of the data gathered, while increasing the robustness and fault tolerance of the overall approach, e.g., using the information offered by V2V communications to provide additional density information in areas where RSUs are scarce or malfunctioning. By using our collaborative sensing scheme, future ITS solutions will be able to establish adequate dissemination protocols or to apply more efficient traffic congestion reduction policies, since they will be aware of the instantaneous density of vehicles.
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paper_title: City traffic prediction based on real-time traffic information for Intelligent Transport Systems

paper_content:

Intelligent Transportation Systems (ITS) have been considered important technologies to mitigate urban traffic congestion. Accurate traffic prediction is one of the critical steps in the operation of an ITS. While techniques for traffic prediction have existed for many years, the research effort has mainly been focused on highway networks. Due to the fundamental difference between the traffic flow pattern on highways and that on city roads, much of the existing models cannot be effectively applied to city traffic prediction. In this paper, we propose two city traffic prediction models using different modeling approaches. Model-1 is based on the traffic flow propagation in the network, while Model-2 is based on the time-varied spare flow capacity on the concerned road link. The proposed models are implemented to predict the traffic volume in Cologne in Germany, and the real data are collected through simulations in the traffic simulator SUMO. The results show that both of the proposed models reduce the prediction error up to 52% and 30% in the best cases compared to the existing Shift Model. In addition, we found that Model-1 is suitable for short prediction interval that is in the same magnitude as the link travel time, while Model-2 demonstrates superiority when the prediction interval is larger than one minute.
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paper_title: Enabling internet of things for smart homes through fog computing

paper_content:

Smart grid is the process of applying ICT in order to optimize energy consumption and decrease energy loses. This paper presents a three tier Internet of Thing based hierarchical framework for the smart home, as a reflection to the present lack of intelligent solutions that do not fully use the advantages of Internet of Thing technologies. Our framework aims to extend the smart home to microgrid level, in order to integrate all renewable distributed energy sources from the microgrid and to achieve better energy optimization. As an extension to the traditional data processing, we define fog computing approach for smart home. Through simulation on real smart meter dataset, we showed that fog computing based on predictive filters can reduce the number of transmissions and minimize smart home network traffic.
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paper_title: Vehicular Fog Computing: Architecture, Use Case, and Security and Forensic Challenges

paper_content:

Vehicular fog computing extends the fog computing paradigm to conventional vehicular networks. This allows us to support more ubiquitous vehicles, achieve better communication efficiency, and address limitations in conventional vehicular networks in terms of latency, location awareness, and real-time response (typically required in smart traffic control, driving safety applications, entertainment services, and other applications). Such requirements are particularly important in adversarial environments (e.g., urban warfare and battlefields in the Internet of Battlefield Things involving military vehicles). However, there is no one widely accepted definition for vehicular fog computing and use cases. Thus, in this article, we formalize the vehicular fog computing architecture and present a typical use case in vehicular fog computing. Then we discuss several key security and forensic challenges and potential solutions.
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paper_title: Real-Time Traffic State Estimation With Connected Vehicles

paper_content:

A novel framework is developed in this paper, to increase the real-time roadway traffic condition assessment accuracy, which integrates connected vehicle technology (CVT) with artificial intelligence (AI) paradigm forming a CVT-AI method. Traffic density is a major indicator of traffic conditions. In this paper, the traffic operational condition is assessed based on traffic density. A simulated network of Interstate 26 in South Carolina is developed to investigate the effectiveness of the method. The assumption is that the vehicle onboard units will forward the CV generated data to the edge devices (e.g., roadside units) for further processing. CV generated distance headway and number of stops, and speed data are used to estimate traffic density. This paper reveals that, with 20% and greater CV penetration levels, the accuracy of the density information with the AI-aided CVT is a minimum of 85%. Moreover, this paper demonstrates that the integrated CVT-AI method yields a higher accuracy with the increase of CV penetration levels. Level of service (LOS) is the indicator of traffic congestion level on highways and is described with traffic density in terms of passenger car/mile/lane for a specific free flow speed. LOS estimated using the CVT-AI density estimation method is compared with the density estimation algorithm used by the Caltrans Performance Measurement System (PeMS), which relies on the occupancy and flow data collected by the freeway inductive loop detectors. With a 10% or more CV penetration, higher accuracy is achieved using the CVT-AI algorithm compared with the PeMS density estimation algorithm.
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paper_title: Energy Dissipation Based Longitudinal and Lateral Coupling Control for Intelligent Vehicles

paper_content:

This paper proposes a combined longitudinal and lateral control approach for an intelligent vehicle system based on energy dissipation. The vehicle system dynamics resembles a series of mass/spring/damper systems that are dissipative, i.e., the energy of the system decays to zero eventually. Thus, the nonlinear-optimal longitudinal and lateral coupling control problem of the intelligent vehicle system is transformed into a dissipative control design based on an energy storage function. To satisfy the g-performance, with respect to the quadratic supply rate, the storage function is developed by using a back-stepping based Lyapunov method and a step-by-step improvement of performance bounds. A dissipative feedback control law is formulated by successive approximation based on the stepby-step reduction of the value of g. The results of the adaptive vehicle control simulations and test-bed experiments are provided and verified by the respective comparison of energy consumption on different values of g and speed adaption under different road geometries.
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Title: Fog Computing for Detecting Vehicular Congestion, An Internet of Vehicles based Approach: A review
Section 1: INTRODUCTION
Description 1: This section discusses the global phenomenon of vehicular congestion, its impact, and the need for an accurate real-time vehicular traffic congestion identification system.

Section 2: VEHICULAR COMPUTING MODELS
Description 2: This section examines various vehicular computing models, focusing on how these models process and handle data in IoV systems.

Section 3: Vehicle using Cloud (VuC)
Description 3: This subsection provides an overview of the Vehicle using Cloud (VuC) approach, highlighting its benefits and limitations for vehicular networks.

Section 4: Mobile Cloud Computing (MCC)
Description 4: This subsection details the Mobile Cloud Computing model, explaining its operational mechanism and suitability for vehicular applications.

Section 5: Vehicular Cloud Computing (VCC)
Description 5: This subsection introduces Vehicular Cloud Computing, emphasizing its autonomous, self-organized nature and applicability in mobile environments.

Section 6: Fog Computing (FC)
Description 6: This subsection explores Fog Computing, a distributed computing approach, and its relevance to vehicular networks, addressing challenges and benefits.

Section 7: VEHICULAR CLOUD AND FOG COMPUTING
Description 7: This section describes the combined architecture of vehicular cloud and fog computing, elaborating on the interactions between data generation, fog nodes, and cloud servers.

Section 8: DETECTION
Description 8: This section discusses various methods and algorithms used for traffic density estimation and vehicular congestion detection in IoV systems.

Section 9: Estimation using Statistical Methods
Description 9: This subsection examines statistical methods for traffic density estimation, discussing different algorithmic approaches and their performance.

Section 10: Estimation using V2V Communication
Description 10: This subsection details V2V communication-based traffic density estimation techniques, focusing on algorithms for local traffic density estimation.

Section 11: Estimation using V2I Communication
Description 11: This subsection highlights V2I communication-based methods for traffic density estimation, explaining the broader coverage area and improved estimation accuracy.

Section 12: DISCUSSION
Description 12: This section analyzes and compares the effectiveness of different vehicular congestion detection techniques and computing paradigms within IoV systems.

Section 13: CONCLUSION
Description 13: This section summarizes the review, highlighting the advantages of CVT and Fog Computing-based traffic density estimation for real-time congestion detection and traffic management.