Generic and Energy-Efficient Context-Aware Mobile Sensing

Elaborating on the concept of context awareness, this book presents up-to-date research and novel framework designs for context-aware mobile sensing. Generic and Energy-Efficient Context-Aware Mobile Sensing proposes novel context-inferring algorithms and generic framework designs that can help readers enhance existing tradeoffs in mobile sensing, especially between accuracy and power consumption.The book presents solutions that emphasize must-have system characteristics such as energy efficiency, accuracy, robustness, adaptability, time-invariance, and optimal sensor sensing. Numerous application examples guide readers from fundamental concepts to the implementation of context-aware-related algorithms and frameworks.Covering theory and practical strategies for context awareness in mobile sensing, the book will help readers develop the modeling and analysis skills required to build futuristic context-aware framework designs for resource-constrained platforms. Includes best practices for designing and implementing practical context-aware frameworks in ubiquitous/mobile sensing Proposes a lightweight online classification method to detect user-centric postural actions Examines mobile device-based battery modeling under the scope of battery nonlinearities with respect to variant loads Unveils a novel discrete time inhomogeneous hidden semi-Markov model (DT-IHS-MM)-based generic framework to achieve a better realization of HAR-based mobile context awareness Supplying theory and equation derivations for all the concepts discussed, the book includes design tips for the implementation of smartphone programming as well as pointers on how to make the best use of MATLAB® for the presentation of performance analysis. Coverage includes lightweight, online, and unsupervised pattern recognition methods; adaptive, time-variant, and optimal sensory sampling strategies; and energy-efficient, robust, and inhomogeneous context-aware framework designs. Researchers will learn the latest modeling and analysis research on mobile sensing. Students will gain access to accessible reference material on mobile sensing theory and practice. Engineers will gain authoritative insights into cutting-edge system designs.

Context Awareness for Mobile SensingIntroductionContext Awareness Essentials Contextual Information Context Representation ContextModeling Context-Aware Middleware Context Inference Context-Aware Framework DesignsContext-Aware ApplicationsHealth Care andWell-Being Based Human Activity Recognition Based Transportation and Location Based Social Networking Based Environmental BasedChallenges and Future Trends Energy Awareness Adaptive and Opportunistic Sensory Sampling Modeling the Smart Device Battery Behavior for Energy Optimizations Data Calibration and Robustness Efficient Context Inference Algorithms Generic Context-Aware Framework Designs Standard Context-Aware Middleware Solutions Mobile Cloud Computing Security, Privacy, and TrustContext Inference: Posture Detection DiscussionsProposed Classification MethodStandalone ModeAssisting Mode Feature Extraction Pattern Recognition–Based Classification Gaussian Mixture Model k-Nearest Neighbors Search Linear Discriminant Analysis Online Processing: Dynamic Training Statistical Tool–Based ClassificationPerformance EvaluationContext-Aware Framework: A Basic DesignDiscussionsProposed Framework Preliminaries User State Representation System Adaptability Time-Variant User State Transition Matrix Time-Variant Observation Emission Matrix Update on System Parameters Entropy Rate Scaling ProblemSimulations Preparations Applied Process Power Consumption Model Accuracy Model Parameter Setups Results and Discussions Validation by a Smartphone Application Observation Analysis Construction of Observation Emission Matrix Applied Process Performance EvaluationEnergy Efficiency in Physical HardwareDiscussionsBattery ModelingModeling of Energy Consumption by Sensors Preliminaries Modeling of Sensory OperationsValidation by a Smartphone ApplicationSensor Management Battery Case Sensor Utilization CasePerformance Analysis Method I (MI) Method II (MII) Method III (MIII) Context-Aware Framework: A Complex DesignProposed FrameworkContext Inference Module Inhomogeneous Statistical Machine Basic Definitions and Inhomogeneity Underlying Process User State Representation Time-Variant User State TransitionMatrix Adaptive Observation Emission Matrix Accuracy Notifier and Definition of ActionsSensor Management Module Sensor Utilization Trade-Off Analysis Intuitive Solutions Method I (MI) Method II (MII) Method III (MIII) Constrained Markov Decision Process–Based Solution Partially Observable Markov DecisionProcess–Based Solution Myopic Strategy and Sufficient StatisticsPerformance EvaluationProbabilistic Context ModelingConstruction of Hidden Markov Models General Model Parallel HMMs Factorial HMMs Coupled/Joint HMMs Observation Decomposed/Multiple Observation HMMs Hierarchical HMMs Dynamic Bayesian NetworksEvaluationInference Learning: Forward–Backward Procedure Extended Forward–Backward ProcedureModel for Multiple Sensors UseAppendixReferences Index

Ozgur Yurur received a double major from the Department of Electronics Engineering and the Department of Computer Engineering at Gebze Institute of Technology, Kocaeli, Turkey, in 2008, and MSEE and PhD from the Department of Electrical Engineering at the University of South Florida (USF), Tampa, Florida, in 2010 and 2013, respectively. He is currently with RF Micro Devices, responsible for the research and design of new test development strategies and also for the implementation of hardware, software, and firmware solutions for 2G, 3G, 4G, and wireless-based company products. In addition, Dr. Yurur conducts research in the field of mobile sensing. His research area covers ubiquitous sensing, mobile computing, machine learning, and energy-efficient optimal sensing policies in wireless networks. The main focus of his research is on developing and implementing accurate, energy-efficient, predictive, robust, and optimal context-aware algorithms and framework designs on sensor-enabled mobile devices.Chi Harold Liu is a full professor at the School of Software, Beijing Institute of Technology, China. He is also the deputy director of IBM Mainframe Excellence Center (Beijing), director of IBM Big Data Technology Center, and director of National Laboratory of Data Intelligence for China Light Industry. He holds a PhD from Imperial College, United Kingdom, and a BEng from Tsinghua University, China. Before moving to academia, he joined IBM Research, China, as a staff researcher and project manager and was previously a postdoctoral researcher at Deutsche Telekom Laboratories, Germany, and a visiting scholar at IBM T. J. Watson Research Center, Armonk, New York. Dr. Liu’s current research interests include the Internet of Things (IoT), big data analytics, mobile computing, and wireless ad hoc, sensor, and mesh networks. He received the IBM First Plateau Invention Achievement Award in 2012 and an IBM First Patent Application Award in 2011. He was interviewed by EEWeb.com as the featured engineer in 2011. Dr. Liu has published more than 50 prestigious conference and journal papers and owns more than 10 EU, U.S., and China patents. He serves as the editor for KSII Transactions on Internet and Information Systems and was book author or editor of three books published by CRC Press. He has served as the general chair of the IEEE SECON’13 workshop on IoT Networking and Control, the IEEEWCNC’12 workshop on IoT Enabling Technologies, and the ACM UbiComp’11Workshop on Networking and Object Memories for IoT. He has also served as a consultant for Bain & Company and KPMG, United States; and as a peer reviewer for Qatar National Research Foundation and the National Science Foundation in China. He is a member of the IEEE and the ACM.