Survey on Prediction Algorithms in Smart Homes
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paper_title: Bio-signal based control in assistive Robots: A survey

paper_content:

Recently, bio-signal based control has been gradually deployed in biomedical devices and assistive robots for improving the quality of life of disabled and elderly people, among which electromyography (EMG) and electroencephalography (EEG) bio-signals are being used widely. This paper reviews the deployment of these bio-signals in the state of art of control systems. The main aim of this paper is to describe the techniques used for (i) collecting EMG and EEG signals and diving these signals into segments (data acquisition and data segmentation stage), (ii) dividing the important data and removing redundant data from the EMG and EEG segments (feature extraction stage), and (iii) identifying categories from the relevant data obtained in the previous stage (classification stage). Furthermore, this paper presents a summary of applications controlled through these two bio-signals and some research challenges in the creation of these control systems. Finally, a brief conclusion is summarized.
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paper_title: The role of prediction algorithms in the MavHome smart home architecture

paper_content:

The goal of the MavHome project is to create a home that acts as a rational agent. The agent seeks to maximize inhabitant comfort and minimize operation cost. To achieve these goals, the agent must be able to predict the mobility patterns and device usages of the inhabitants. We introduce the MavHome project and its underlying architecture. The role of prediction algorithms within the architecture is discussed, and three prediction algorithms that are central to home operations are presented. We demonstrate the effectiveness of these algorithms on synthetic and/or actual smart home data.
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paper_title: A Review of Smart Homes—Past, Present, and Future

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A smart home is an application of ubiquitous computing in which the home environment is monitored by ambient intelligence to provide context-aware services and facilitate remote home control. This paper presents an overview of previous smart home research as well as the associated technologies. A brief discussion on the building blocks of smart homes and their interrelationships is presented. It describes collective information about sensors, multimedia devices, communication protocols, and systems, which are widely used in smart home implementation. Special algorithms from different fields and their significance are explained according to their scope of use in smart homes. This paper also presents a concrete guideline for future researchers to follow in developing a practical and sustainable smart home.
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paper_title: MavHome: an agent-based smart home

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The goal of the MavHome (Managing An Intelligent Versatile Home) project is to create a home that acts as an intelligent agent. In this paper we introduce the MavHome architecture. The role of prediction algorithms within the architecture is discussed, and a meta-predictor is presented which combines the strengths of multiple approaches to inhabitant action prediction. We demonstrate the effectiveness of these algorithms on smart home data.
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paper_title: Activity Recognition in the Home Using Simple and Ubiquitous Sensors

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In this work, a system for recognizing activities in the home setting using a set of small and simple state-change sensors is introduced. The sensors are designed to be “tape on and forget” devices that can be quickly and ubiquitously installed in home environments. The proposed sensing system presents an alternative to sensors that are sometimes perceived as invasive, such as cameras and microphones. Unlike prior work, the system has been deployed in multiple residential environments with non-researcher occupants. Preliminary results on a small dataset show that it is possible to recognize activities of interest to medical professionals such as toileting, bathing, and grooming with detection accuracies ranging from 25% to 89% depending on the evaluation criteria used.
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paper_title: Behavioural pattern identification and prediction in intelligent environments

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In this paper, the application of soft computing techniques in prediction of an occupant's behaviour in an inhabited intelligent environment is addressed. In this research, daily activities of elderly people who live in their own homes suffering from dementia are studied. Occupancy sensors are used to extract the movement patterns of the occupant. The occupancy data is then converted into temporal sequences of activities which are eventually used to predict the occupant behaviour. To build the prediction model, different dynamic recurrent neural networks are investigated. Recurrent neural networks have shown a great ability in finding the temporal relationships of input patterns. The experimental results show that non-linear autoregressive network with exogenous inputs model correctly extracts the long term prediction patterns of the occupant and outperformed the Elman network. The results presented here are validated using data generated from a simulator and real environments.
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paper_title: Predicting inhabitant action using action and task models with application to smart homes

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An intelligent home is likely in the near future. An important ingredient in an intelligent environment such as a home is prediction – of the next low-level action, the next location, and the next high-level task that an inhabitant is likely to perform. In this paper we model inhabitant actions as states in a simple Markov model. We introduce an enhancement to this basic approach, the Task-based Markov model (TMM) method. TMM discovers high-level inhabitant tasks using the supplied unlabeled data. We investigate clustering of actions to identify tasks, and integrate clusters into a hidden Markov model that predicts the next inhabitant action. We validate our approach and observe that for simulated data we achieve good accuracy using both the simple Markov model and the TMM, whereas on real data we see that simple Markov models outperform the TMM. We also perform an analysis of the performance of the HMM in the framework of the TMM when diverse patterns are introduced into the data.
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paper_title: Bayesian Networks Structure Learning for Activity Prediction in Smart Homes

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This paper presents a sequence-based activity prediction approach which uses Bayesian networks in a novel two-step process to predict both activities and their corresponding features. In addition to the proposed model, we also present the results of several search and score (SaS) and constraint-based (CB) Bayesian structure learning algorithms. The activity prediction performance of the proposed model is compared with the naive Bayes and the other aforementionedSaS and CB algorithms. The experimental results are performed on real data collected from a smart home over the period of five months. The results suggest the superior activity prediction accuracy of the proposed network over the resulting networks of the mentioned Bayesian network structure learning algorithms.
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paper_title: Discovering models of software processes from event-based data

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Many software process methods and tools presuppose the existence of a formal model of a process. Unfortunately, developing a formal model for an on-going, complex process can be difficult, costly, and error prone. This presents a practical barrier to the adoption of process technologies, which would be lowered by automated assistance in creating formal models. To this end, we have developed a data analysis technique that we term process discovery. Under this technique, data describing process events are first captured from an on-going process and then used to generate a formal model of the behavior of that process. In this article we describe a Markov method that we developed specifically for process discovery, as well as describe two additional methods that we adopted from other domains and augmented for our purposes. The three methods range from the purely algorithmic to the purely statistical. We compare the methods and discuss their application in an industrial case study.
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paper_title: Modeling human behavior from simple sensors in the home

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Pervasive sensors in the home have a variety of applications including energy minimization, activity monitoring for elders, and tutors for household tasks such as cooking. Many of the common sensors today are binary, e.g. IR motion sensors, door close sensors, and floor pressure pads. Predicting user behavior is one of the key enablers for applications. While we consider smart home data here, the general problem is one of predicting discrete human actions. Drawing on Activity Theory, the language as action principle, and speech understanding research, we argue that smoothed n-grams are very appropriate for this task. We built such a model and applied it to data gathered from 3 smart home installations. The data showed a classic Zipf or power-law distribution, similar to speech and language. We found that the predictive accuracy of the n-gram model ranges from 51% to 39%, which is significantly above the baseline for the deployments of 16, 76 and 70 sensors. While we cannot directly compare this result with other work (lack of shared data), by examination of high entropy zones in the datasets (e.g. the kitchen triangle) we argue that accuracies around 50% are best possible for this task.
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paper_title: A tutorial on hidden Markov models and selected applications in speech recognition

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This tutorial provides an overview of the basic theory of hidden Markov models (HMMs) as originated by L.E. Baum and T. Petrie (1966) and gives practical details on methods of implementation of the theory along with a description of selected applications of the theory to distinct problems in speech recognition. Results from a number of original sources are combined to provide a single source of acquiring the background required to pursue further this area of research. The author first reviews the theory of discrete Markov chains and shows how the concept of hidden states, where the observation is a probabilistic function of the state, can be used effectively. The theory is illustrated with two simple examples, namely coin-tossing, and the classic balls-in-urns system. Three fundamental problems of HMMs are noted and several practical techniques for solving these problems are given. The various types of HMMs that have been studied, including ergodic as well as left-right models, are described. >
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paper_title: AN INTRODUCTION TO HIDDEN MARKOV MODELS AND BAYESIAN NETWORKS

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We provide a tutorial on learning and inference in hidden Markov models in the context of the recent literature on Bayesian networks. This perspective make sit possible to consider novel generalizations to hidden Markov models with multiple hidden state variables, multiscale representations, and mixed discrete and continuous variables. Although exact inference in these generalizations is usually intractable, one can use approximate inference in these generalizations is usually intractable, one can use approximate inference algorithms such as Markov chain sampling and variational methods. We describe how such methods are applied to these generalized hidden Markov models. We conclude this review with a discussion of Bayesian methods for model selection in generalized HMMs.
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paper_title: Location aware resource management in smart homes

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The rapid advances in a wide range of wireless access technologies along with the efficient use of smart spaces have already set the stage for the development of smart homes. Context-awareness is perhaps the most salient feature in these intelligent computing platforms. The "location" information of the users plays a vital role in defining this context. To extract the best performance and efficacy of such smart computing environments, one needs a scalable, technology-independent location service. We have developed a predictive framework for location-aware resource optimization in smart homes. The underlying compression mechanism helps in efficient learning of an inhabitant's movement (location) profiles in the symbolic domain. The concept of Asymptotic Equipartition Property (AEP) in information theory helps to predict the inhabitant's future location as well as most likely path-segments with good accuracy. Successful prediction helps in pro-active resource management and on-demand operations of automated devices along the inhabitant's future paths and locations - thus providing the necessary comfort at a near-optimal cost. Simulation results on a typical smart home floor plans corroborate this high prediction success and demonstrate sufficient reduction in daily energy-consumption, manual operations and time spent by the inhabitant which are considered as a fair measure of his/her comfort.
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paper_title: Energy storage management in smart homes based on resident activity of daily life recognition

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Recently, home energy storage system is emerging as one of the main driving forces to prompt the development of the future smart grid. By leveraging time-based electricity pricing, the home energy storage system can store energy during off-peak periods and supply energy to residential customers during on-peak periods, such that the stress on main power system can be relieved. Yet, an efficient use of home energy storage system is still a challenging issue, due to the nonlinear properties of the battery in terms of energy conversion loss and shortened battery life, and the randomness in residential energy demand. In this paper, we investigate the utilization of smart home monitoring and communication technologies to recognize the resident activity of daily life (ADL) and propose a non-homogeneous hidden Markov model (NHMM) to characterize the residential energy demand. An optimal energy storage management problem is formulated by taking into account the NHMM and nonlinear battery properties. This problem belongs to a class of adaptive stochastic control problems in smart grid with nonlinear value functions. In order to solve this problem efficiently, piecewise linear approximation is applied to the energy conversion function, and a state-dependent multi-threshold policy is proposed and proved to be optimal. The performance of the proposed energy management scheme is evaluated via a case study based on CASAS smart home dataset collected in real life by Washington State University. Numerical results indicate that our proposed energy storage management scheme can achieve energy cost savings, in comparison with existing schemes with uniform and non-uniform discharging profiles.
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paper_title: Using duration to learn activities of daily living in a smart home environment

paper_content:

Recognition of inhabitants' activities of daily living (ADLs) is an important task in smart homes to support assisted living for elderly people aging in place. However, uncertain information brings challenge to activity recognition which can be categorised into environmental uncertainties from sensor readings and user uncertainties of variations in the ways to carry out activities in different contexts, or by different users within the same environment. To address the challenges of these two types of uncertainty, in this paper, we introduce the innovative idea of incorporating activity duration into the framework of learning inhabitants' behaviour patterns on carrying out ADLs in smart home environment. A probabilistic learning algorithm is proposed with duration information in the context of multi-inhabitants in a single home environment. The prediction is for both inhabitant and ADL using the learned model representing what activity is carried out and who performed it. Experiments are designed for the evaluation of duration information in identifying activities and inhabitants. Real data have been collected in a smart kitchen laboratory, and realistic synthetic data are generated for evaluation. Evaluations show encouraging results for higher-level activity identification and improvement on inhabitant and activity prediction in the challenging situation of incomplete observation due to unreliable sensors compared to models that are derived with no duration information. The approach also provides a potential opportunity to identify inhabitants' concept drift in long-term monitoring and respond to a deteriorating situation at as early stage as possible.
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paper_title: A Knowledge-Driven Approach to Activity Recognition in Smart Homes

paper_content:

This paper introduces a knowledge-driven approach to real-time, continuous activity recognition based on multisensor data streams in smart homes. The approach goes beyond the traditional data-centric methods for activity recognition in three ways. First, it makes extensive use of domain knowledge in the life cycle of activity recognition. Second, it uses ontologies for explicit context and activity modeling and representation. Third and finally, it exploits semantic reasoning and classification for activity inferencing, thus enabling both coarse-grained and fine-grained activity recognition. In this paper, we analyze the characteristics of smart homes and Activities of Daily Living (ADL) upon which we built both context and ADL ontologies. We present a generic system architecture for the proposed knowledge-driven approach and describe the underlying ontology-based recognition process. Special emphasis is placed on semantic subsumption reasoning algorithms for activity recognition. The proposed approach has been implemented in a function-rich software system, which was deployed in a smart home research laboratory. We evaluated the proposed approach and the developed system through extensive experiments involving a number of various ADL use scenarios. An average activity recognition rate of 94.44 percent was achieved and the average recognition runtime per recognition operation was measured as 2.5 seconds.
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paper_title: Intervention of non-inhabitant activities detection in smart home environment

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Inhabitants daily activity form a pattern in their daily life which has important things in smart home. These patterns can be used to recognize the inhabitant activity that is useful to enhance the smart home services like energy efficiency service, where these patterns can be used as inhabitant behavior to reduce an unnecessary appliances or lightings usage based on the activities their conduct. Recognition accuracy is important things for providing particular service needs on automation process in smart home, but activity recognition faces many challenges in real world cause of diversity and complexity of the activities. Inter-subject variability activities often appear in real world situation that accuracy of recognition process can be affected. For instance, there is a possible situation where family or colleague visits to inhabitant's home in long term. Non-inhabitant activities may conduct with a different way or different behavior than inhabitant does. This situation is producing activities where is not carried from legitimate inhabitant. In this paper, we propose a method to overcome the activity recognition issue that commonly occurred. Our proposed method using temporal relation approach, which can detect a non-inhabitant activity. This approach is separating detected activities from inhabitant's observed activities, so the activity recognition will perform effectively. We assess the effectiveness of our approach using Activity Daily Living (ADL) provided by WSU Smart Home Project dataset.
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paper_title: Monitoring Activities of Daily Living in Smart Homes: Understanding human behavior

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Monitoring the activities of daily living (ADLs) and detection of deviations from previous patterns is crucial to assessing the ability of an elderly person to live independently in their community and in early detection of upcoming critical situations. "Aging in place" for an elderly person is one key element in ambient assisted living (AAL) technologies.
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paper_title: Use of Prediction Algorithms in Smart Homes

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Smart Homes' or 'Intelligent Homes' are capable in making smart or rational decisions and increase home automation. This is done to maximize inhabitant comfort and minimize operation cost. Tracing and predicting the mobility patterns and usages of devices by the inhabitant, sets a step towards the objective. The paper discusses in detail, the role of certain Prediction algorithms to bring about next event recognition. Further, an Episode Discovery helps in finding the frequency of occurrence of these events and targeting the particular events for automation. The effectiveness of the Prediction algorithms used is demonstrated ;making it clear how they prove to be a key component in the efficient implementation of a Smart Home architecture.
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paper_title: Learning to Control a Smart Home Environment

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The goal of the MavHome (Managing An Intelligent Versatile Home) project is to create a home that acts as an intelligent agent. In this paper, we introduce the MavHome architecture and two learning algorithms that play central roles in the smart home. The first algorithm predicts actions the inhabitant will take in the home. The second algorithm learns a policy to control the home. Effectiveness of the algorithms on smart home data is presented and we document the application of the technology in a working smart home environment.
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paper_title: LeZi-update: an information-theoretic approach to track mobile users in PCS networks

paper_content:

The complexity of the mobility tracking problem in a cellular environment has been characterized under an informationtheoretic framework. Shannon’s entropy measure is identified as a basis for comparing user mobility models. By building and maintaining a dictionary of individual user’s path updates (as opposed to the widely used location updates), the proposed adaptive on-line algorithm can learn subscribers’ profiles. This technique evolves out of the concepts of lossless compression. The compressibility of the variable-to-fixed length encoding of the acclaimed LempelZiv family of algorithms reduces the update cost, whereas their built-in predictive power can be effectively used to reduce paging cost.
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paper_title: Active Lezi: An incremental parsing algorithm for sequential prediction

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Prediction is an important component in a variety of domains in Artificial Intelligence and Machine Learning, in order that Intelligent Systems may make more informed and reliable decisions. Certain domains require that prediction be performed on sequences of events that can typically be modeled as stochastic processes. This work presents Active LeZi (ALZ), a sequential prediction algorithm that is founded on an Information Theoretic approach, and is based on the acclaimed LZ78 family of data compression algorithms. The efficacy of this algorithm in a typical Smart Environment – the Smart Home, is demonstrated by employing this algorithm to predict device usage in the home. The performance of this algorithm is tested on synthetic data sets that are representative of typical interactions between a Smart Home and the inhabitant. In addition, for the Smart Home environment, we introduce a method of learning a measure of the relative time between actions using ALZ, and demonstrate the efficacy of this approach on synthetic Smart Home data.
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paper_title: An Improved Position Prediction Algorithm Based on Active LeZi in Smart Home

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In smart home environments, each inhabitant has their own habits, these habits have certain periodic and time-varying characteristics. According to these characteristics, an improved Time-varying LeZi algorithm (TALZ) is proposed, which is based on Active LeZi algorithm (ALZ). Compared to other models, the results show that TALZ has smaller time complexity, higher prediction accuracy of laboratory data acquisition, and it is suitable for real-time, accurate position prediction in smart home environments.
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paper_title: Compression of Individual Sequences via Variable-Rate Coding

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Compressibility of individual sequences by the class of generalized finite-state information-lossless encoders is investigated. These encoders can operate in a variable-rate mode as well as a fixed-rate one, and they allow for any finite-state scheme of variable-length-to-variable-length coding. For every individual infinite sequence x a quantity \rho(x) is defined, called the compressibility of x , which is shown to be the asymptotically attainable lower bound on the compression ratio that can be achieved for x by any finite-state encoder. This is demonstrated by means of a constructive coding theorem and its converse that, apart from their asymptotic significance, also provide useful performance criteria for finite and practical data-compression tasks. The proposed concept of compressibility is also shown to play a role analogous to that of entropy in classical information theory where one deals with probabilistic ensembles of sequences rather than with individual sequences. While the definition of \rho(x) allows a different machine for each different sequence to be compressed, the constructive coding theorem leads to a universal algorithm that is asymptotically optimal for all sequences.
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paper_title: A customized flocking algorithm for swarms of sensors tracking a swarm of targets

paper_content:

Wireless mobile sensor networks (WMSNs) are groups of mobile sensing agents with multi-modal sensing capabilities that communicate over wireless networks. WMSNs have more flexibility in terms of deployment and exploration abilities over static sensor networks. Sensor networks have a wide range of applications in security and surveillance systems, environmental monitoring, data gathering for network-centric healthcare systems, monitoring seismic activities and atmospheric events, tracking traffic congestion and air pollution levels, localization of autonomous vehicles in intelligent transportation systems, and detecting failures of sensing, storage, and switching components of smart grids. The above applications require target tracking for processes and events of interest occurring in an environment. Various methods and approaches have been proposed in order to track one or more targets in a pre-defined area. Usually, this turns out to be a complicated job involving higher order mathematics coupled with artificial intelligence due to the dynamic nature of the targets. To optimize the resources we need to have an approach that works in a more straightforward manner while resulting in fairly satisfactory data. In this paper we have discussed the various cases that might arise while flocking a group of sensors to track targets in a given environment. The approach has been developed from scratch although some basic assumptions have been made keeping in mind some previous theories. This paper outlines a customized approach for feasibly tracking swarms of targets in a specific area so as to minimize the resources and optimize tracking efficiency.
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paper_title: A Knowledge-Driven Approach to Activity Recognition in Smart Homes

paper_content:

This paper introduces a knowledge-driven approach to real-time, continuous activity recognition based on multisensor data streams in smart homes. The approach goes beyond the traditional data-centric methods for activity recognition in three ways. First, it makes extensive use of domain knowledge in the life cycle of activity recognition. Second, it uses ontologies for explicit context and activity modeling and representation. Third and finally, it exploits semantic reasoning and classification for activity inferencing, thus enabling both coarse-grained and fine-grained activity recognition. In this paper, we analyze the characteristics of smart homes and Activities of Daily Living (ADL) upon which we built both context and ADL ontologies. We present a generic system architecture for the proposed knowledge-driven approach and describe the underlying ontology-based recognition process. Special emphasis is placed on semantic subsumption reasoning algorithms for activity recognition. The proposed approach has been implemented in a function-rich software system, which was deployed in a smart home research laboratory. We evaluated the proposed approach and the developed system through extensive experiments involving a number of various ADL use scenarios. An average activity recognition rate of 94.44 percent was achieved and the average recognition runtime per recognition operation was measured as 2.5 seconds.
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paper_title: A Minimalist Flocking Algorithm for Swarm Robots

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In this paper we describe a low-end and easy to implement flocking algorithm which was developed for very simple swarm robots and which works without communication, memory or global information. By adapting traditional flocking algorithms and eliminating the need for communication, we created an algorithm with emergent flocking properties. We analyse its potential of aggregating an initially scattered robot swarm, which is not a trivial task for robots that only have local information.
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paper_title: SPEED: An Inhabitant Activity Prediction Algorithm for Smart Homes

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This paper proposes an algorithm, called sequence prediction via enhanced episode discovery (SPEED), to predict inhabitant activity in smart homes. SPEED is a variant of the sequence prediction algorithm. It works with the episodes of smart home events that have been extracted based on the on -off states of home appliances. An episode is a set of sequential user activities that periodically occur in smart homes. The extracted episodes are processed and arranged in a finite-order Markov model. A method based on prediction by partial matching (PPM) algorithm is applied to predict the next activity from the previous history. The result shows that SPEED achieves an 88.3% prediction accuracy, which is better than LeZi Update, Active LeZi, IPAM, and C4.5.
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paper_title: Nash Q-Learning for General-Sum Stochastic Games

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We extend Q-learning to a noncooperative multiagent context, using the framework of general-sum stochastic games. A learning agent maintains Q-functions over joint actions, and performs updates based on assuming Nash equilibrium behavior over the current Q-values. This learning protocol provably converges given certain restrictions on the stage games (defined by Q-values) that arise during learning. Experiments with a pair of two-player grid games suggest that such restrictions on the game structure are not necessarily required. Stage games encountered during learning in both grid environments violate the conditions. However, learning consistently converges in the first grid game, which has a unique equilibrium Q-function, but sometimes fails to converge in the second, which has three different equilibrium Q-functions. In a comparison of offline learning performance in both games, we find agents are more likely to reach a joint optimal path with Nash Q-learning than with a single-agent Q-learning method. When at least one agent adopts Nash Q-learning, the performance of both agents is better than using single-agent Q-learning. We have also implemented an online version of Nash Q-learning that balances exploration with exploitation, yielding improved performance.
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paper_title: Context-aware resource management in multi-inhabitant smart homes a Nash H-learning based approach

paper_content:

A smart home aims at building intelligence automation with a goal to provide its inhabitants with maximum possible comfort, minimize the resource consumption and thus overall cost of maintaining the home. 'Context awareness' is perhaps the most salient feature of such an intelligent environment. Clearly, an inhabitant's mobility and activities play a significant role in defining his contexts in and around the home. Although there exists an optimal algorithm for location and activity tracking of a single inhabitant, the correlation and dependence between multiple inhabitants' contexts within the same environment make the location and activity tracking more challenging. In this paper, we first prove that the optimal location prediction across multiple inhabitants in smart homes is an NP-hard problem. Next, to capture the correlation and interactions of different inhabitants' movements (and hence activities), we develop a novel framework based on a game theoretic, Nash H-learning approach that attempts to minimize the joint location uncertainty. The framework achieves a Nash equilibrium such that no inhabitant is given preference over others. This results in more accurate prediction of contexts and better adaptive control of automated devices, leading to a mobility-aware resource (say, energy) management scheme in multi-inhabitant smart homes. Experimental results demonstrate that the proposed framework is capable of adaptively controlling a smart environment, thus reducing energy consumption and enhancing the comfort of the inhabitants.
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paper_title: Research on Mining Association Behavior of Smart Home Users Based on Apriori Algorithm

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This paper mainly studies the improvement of Apriori algorithm based on smart home system. Association rule algorithm has the promotion effect to the analysis of the smart home user behavior. Apriori algorithm is one of the classical algorithms of association rules. However, the efficiency of the Apriori algorithm is not high, there is stillroom for improvement in smart home. Based on the characteristics of smart home system and terminal operation platform, this paper optimizes the Apriori algorithm. By introducing the auxiliary matrix, the number of scanning database is reduced, and the speed of the support solving is accelerated. Simulation experiment results show that the Apriori algorithm optimized is more efficient than the optimal algorithm. In rapid advocacy of the Internet of things,with the improvement of people's living standards and housing conditions, digital intelligent have walk into people's life. Smart home products have already entered the vision of ordinary families and greatly simplified people's life. Which make it possible to develop a new lifestyle. Smart home products can not just stay in the original remote control mode. Packing multiple behaviors ofusers and concurrent operation will further streamline operations that improve the intelligence level. Custom profile is a manifestation. Technology product is continually improving the experience of users and to simplify user operations, which is the next target of smart products. According to the user's behavior,mining high degree of user behavior associatedthrough data mining analysis and then recommended as a personalized user profile. Which replacing the custom profiles of trouble, adding intelligence andmeet user preferences in operation. In data mining, association ruleis an importantissue. Association rules can find the existence of things that may exist or contactfrom the data behind. Which could be applied to the association study of user behavior in the smart home. The most classical association rule algorithm is Apriori algorithm. Apriori algorithm is an iterative manner based mining method by searching frequent item set. The mining process of Apriori algorithm is complex because of the iterative. Based on the characteristics of smart home system, this paper proposes an improved method of Apriori algorithm, which makes it more suitable for the implementation and application of the terminal platform.
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paper_title: Keeping the Resident in the Loop: Adapting the Smart Home to the User

paper_content:

Advancements in supporting fields have increased the likelihood that smart-home technologies will become part of our everyday environments. However, many of these technologies are brittle and do not adapt to the user's explicit or implicit wishes. Here, we introduce CASAS, an adaptive smart-home system that utilizes machine learning techniques to discover patterns in resident's daily activities and to generate automation polices that mimic these patterns. Our approach does not make any assumptions about the activity structure or other underlying model parameters but leaves it completely to our algorithms to discover the smart-home resident's patterns. Another important aspect of CASAS is that it can adapt to changes in the discovered patterns based on the resident implicit and explicit feedback and can automatically update its model to reflect the changes. In this paper, we provide a description of the CASAS technologies and the results of experiments performed on both synthetic and real-world data.
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paper_title: A unified view of the apriori-based algorithms for frequent episode discovery

paper_content:

Frequent episode discovery framework is a popular framework in temporal data mining with many applications. Over the years, many different notions of frequencies of episodes have been proposed along with different algorithms for episode discovery. In this paper, we present a unified view of all the apriori-based discovery methods for serial episodes under these different notions of frequencies. Specifically, we present a unified view of the various frequency counting algorithms. We propose a generic counting algorithm such that all current algorithms are special cases of it. This unified view allows one to gain insights into different frequencies, and we present quantitative relationships among different frequencies. Our unified view also helps in obtaining correctness proofs for various counting algorithms as we show here. It also aids in understanding and obtaining the anti-monotonicity properties satisfied by the various frequencies, the properties exploited by the candidate generation step of any apriori-based method. We also point out how our unified view of counting helps to consider generalization of the algorithm to count episodes with general partial orders.
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paper_title: Improving home automation by discovering regularly occurring device usage patterns

paper_content:

The data stream captured by recording inhabitant-device interactions in an environment can be mined to discover significant patterns, which an intelligent agent could use to automate device interactions. However, this knowledge discovery problem is complicated by several challenges, such as excessive noise in the data, data that does not naturally exist as transactions, a need to operate in real time, and a domain where frequency may not be the best discriminator. We propose a novel data mining technique that addresses these challenges and discovers regularly-occurring interactions with a smart home. We also discuss a case study that shows the data mining technique can improve the accuracy of two prediction algorithms, thus demonstrating multiple uses for a home automation system. Finally, we present an analysis of the algorithm and results obtained using inhabitant interactions.
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paper_title: A Time Series Based Sequence Prediction Algorithm to Detect Activities of Daily Living in Smart Home

paper_content:

OBJECTIVES ::: The goal of smart homes is to create an intelligent environment adapting the inhabitants need and assisting the person who needs special care and safety in their daily life. This can be reached by collecting the ADL (activities of daily living) data and further analysis within existing computing elements. In this research, a very recent algorithm named sequence prediction via enhanced episode discovery (SPEED) is modified and in order to improve accuracy time component is included. ::: ::: ::: METHODS ::: The modified SPEED or M-SPEED is a sequence prediction algorithm, which modified the previous SPEED algorithm by using time duration of appliance's ON-OFF states to decide the next state. M-SPEED discovered periodic episodes of inhabitant behavior, trained it with learned episodes, and made decisions based on the obtained knowledge. ::: ::: ::: RESULTS ::: The results showed that M-SPEED achieves 96.8% prediction accuracy, which is better than other time prediction algorithms like PUBS, ALZ with temporal rules and the previous SPEED. ::: ::: ::: CONCLUSIONS ::: Since human behavior shows natural temporal patterns, duration times can be used to predict future events more accurately. This inhabitant activity prediction system will certainly improve the smart homes by ensuring safety and better care for elderly and handicapped people.
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paper_title: TEMPORAL MODELING OF HUMAN ACTIVITY IN SMART HOMES

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Recognition of human activity is a potential challenge to design an effective smart home. The paper proposed a novel algorithm to recognize activities of daily living (ADL) of the resident. It provides analysis and mathematical modeling of temporal intervals of the event. The opposite entity states are used to extract the pattern of event sequence. Each extracted episode represents a distinct task of the resident. Result shows that, the algorithm can successfully identify 135 unique tasks of different lengths with temporal characteristics. The analysis confirms that temporal pattern follows normal distribution which can be modeled by Gaussian function.
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paper_title: A Novel Deployment Scheme for Green Internet of Things

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The Internet of Things (IoT) has been realized as one of the most promising networking paradigms that bridge the gap between the cyber and physical world. Developing green deployment schemes for IoT is a challenging issue since IoT achieves a larger scale and becomes more complex so that most of the current schemes for deploying wireless sensor networks (WSNs) cannot be transplanted directly in IoT. This paper addresses this challenging issue by proposing a deployment scheme to achieve green networked IoT. The contributions made in this paper include: 1) a hierarchical system framework for a general IoT deployment, 2) an optimization model on the basis of proposed system framework to realize green IoT, and 3) a minimal energy consumption algorithm for solving the presented optimization model. The numerical results on minimal energy consumption and network lifetime of the system indicate that the deployment scheme proposed in this paper is more flexible and energy efficient compared to typical WSN deployment scheme; thus is applicable to the green IoT deployment.
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paper_title: Toward Efficient Multi-Keyword Fuzzy Search Over Encrypted Outsourced Data With Accuracy Improvement

paper_content:

Keyword-based search over encrypted outsourced data has become an important tool in the current cloud computing scenario. The majority of the existing techniques are focusing on multi-keyword exact match or single keyword fuzzy search. However, those existing techniques find less practical significance in real-world applications compared with the multi-keyword fuzzy search technique over encrypted data. The first attempt to construct such a multi-keyword fuzzy search scheme was reported by Wang et al. , who used locality-sensitive hashing functions and Bloom filtering to meet the goal of multi-keyword fuzzy search. Nevertheless, Wang’s scheme was only effective for a one letter mistake in keyword but was not effective for other common spelling mistakes. Moreover, Wang’s scheme was vulnerable to server out-of-order problems during the ranking process and did not consider the keyword weight. In this paper, based on Wang et al. ’s scheme, we propose an efficient multi-keyword fuzzy ranked search scheme based on Wang et al. ’s scheme that is able to address the aforementioned problems. First, we develop a new method of keyword transformation based on the uni-gram, which will simultaneously improve the accuracy and creates the ability to handle other spelling mistakes. In addition, keywords with the same root can be queried using the stemming algorithm. Furthermore, we consider the keyword weight when selecting an adequate matching file set. Experiments using real-world data show that our scheme is practically efficient and achieve high accuracy.
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paper_title: Incremental learning for ν -Support Vector Regression

paper_content:

The ? -Support Vector Regression ( ? -SVR) is an effective regression learning algorithm, which has the advantage of using a parameter ? on controlling the number of support vectors and adjusting the width of the tube automatically. However, compared to ? -Support Vector Classification ( ? -SVC) (Scholkopf et?al., 2000), ? -SVR introduces an additional linear term into its objective function. Thus, directly applying the accurate on-line ? -SVC algorithm (AONSVM) to ? -SVR will not generate an effective initial solution. It is the main challenge to design an incremental ? -SVR learning algorithm. To overcome this challenge, we propose a special procedure called initial adjustments in this paper. This procedure adjusts the weights of ? -SVC based on the Karush-Kuhn-Tucker (KKT) conditions to prepare an initial solution for the incremental learning. Combining the initial adjustments with the two steps of AONSVM produces an exact and effective incremental ? -SVR learning algorithm (INSVR). Theoretical analysis has proven the existence of the three key inverse matrices, which are the cornerstones of the three steps of INSVR (including the initial adjustments), respectively. The experiments on benchmark datasets demonstrate that INSVR can avoid the infeasible updating paths as far as possible, and successfully converges to the optimal solution. The results also show that INSVR is faster than batch ? -SVR algorithms with both cold and warm starts.
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Title: Survey on Prediction Algorithms in Smart Homes

Section 1: Introduction
Description 1: Introduce the concept of smart homes and the importance of prediction algorithms in enabling intelligent control and automation.

Section 2: Background of Prediction Algorithms
Description 2: Provide background information on prediction algorithms, including system models and data used in the context of smart homes.

Section 3: System Models
Description 3: Discuss various system models that are pertinent to prediction algorithms in smart homes, such as Markov models and Bayesian networks.

Section 4: Activities of Daily Living (ADLs)
Description 4: Explain how prediction algorithms utilize the concept of activities of daily living (ADLs) to forecast future events based on historical data in smart home environments.

Section 5: Location Prediction
Description 5: Discuss the importance of predicting the location of inhabitants within a smart environment and various methodologies used for this task.

Section 6: Review of Prediction Algorithms
Description 6: Extensively review several prediction algorithms used in smart homes, highlighting their features, strengths, and weaknesses.

Section 7: Active LeZi
Description 7: Detail the technical aspects and application of the Active LeZi algorithm in smart environments.

Section 8: Flocking
Description 8: Describe the flocking algorithm, its basis in clustering techniques, and its applications in smart homes.

Section 9: SPEED
Description 9: Examine the SPEED algorithm and how it uses periodic tendencies to predict user activities in smart homes.

Section 10: Nash H-Learning
Description 10: Analyze the Nash H-learning algorithm, focusing on its applications in predicting the location of multiple users within a smart home.

Section 11: Apriori Algorithm
Description 11: Explain the Apriori algorithm and its enhancements for use in smart home environments.

Section 12: Summary of Algorithms
Description 12: Summarize and compare the reviewed prediction algorithms, emphasizing their similarities and differences in approach and data handling.

Section 13: Algorithm Enhancements
Description 13: Discuss various enhancements and extensions to prediction algorithms that aim to improve performance in smart home applications.

Section 14: Episode Discovery
Description 14: Describe the episode discovery method and its role in enhancing prediction algorithms by identifying significant patterns in the data.

Section 15: Temporal Relations
Description 15: Explain the importance of temporal relations in improving prediction accuracy and anomaly detection within smart home environments.

Section 16: Conclusion
Description 16: Summarize the key points discussed in the paper and highlight the significance of prediction algorithms in the development of intelligent smart home systems.