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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.
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Product details

  • Paperback
  • 155.58 x 234.95mm
  • CRC Press
  • English
  • 1138894516
  • 9781138894518


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 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.
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Table of contents

Context Awareness for Mobile SensingIntroductionContext Awareness Essentials     Contextual Information     Context Representation     ContextModeling     Context-Aware Middleware     Context Inference     Context-Aware Framework DesignsContext-Aware Applications     Health 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
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