1st Edition

Generic and Energy-Efficient Context-Aware Mobile Sensing

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ISBN 9781498700108
Published February 2, 2015 by CRC Press
221 Pages 41 B/W Illustrations

USD $175.00

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Book Description

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.

Table of Contents

Context Awareness for Mobile Sensing
Context Awareness Essentials
     Contextual Information
     Context Representation
     Context-Aware Middleware
     Context Inference
     Context-Aware Framework Designs
Context-Aware Applications
     Health Care andWell-Being Based
     Human Activity Recognition Based
     Transportation and Location Based
     Social Networking Based
     Environmental Based
Challenges 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 Trust

Context Inference: Posture Detection
Proposed Classification Method
Standalone Mode
Assisting Mode
     Feature Extraction
     Pattern Recognition–Based Classification
          Gaussian Mixture Model
          k-Nearest Neighbors Search
          Linear Discriminant Analysis
     Online Processing: Dynamic Training 
     Statistical Tool–Based Classification
Performance Evaluation

Context-Aware Framework: A Basic Design
Proposed Framework
     User State Representation
     System Adaptability 
          Time-Variant User State Transition Matrix
          Time-Variant Observation Emission Matrix
          Update on System Parameters
          Entropy Rate
          Scaling Problem
     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 Evaluation

Energy Efficiency in Physical Hardware
Battery Modeling
Modeling of Energy Consumption by Sensors
     Modeling of Sensory Operations
Validation by a Smartphone Application
Sensor Management
     Battery Case
     Sensor Utilization Case
Performance Analysis
     Method I (MI)
     Method II (MII)
     Method III (MIII)

Context-Aware Framework: A Complex Design
Proposed Framework
Context 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 Actions
Sensor 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 Decision
Process–Based Solution
          Myopic Strategy and Sufficient Statistics
Performance Evaluation

Probabilistic Context Modeling
Construction of Hidden Markov Models
     General Model
     Parallel HMMs
     Factorial HMMs
     Coupled/Joint HMMs
     Observation Decomposed/Multiple Observation HMMs
     Hierarchical HMMs
     Dynamic Bayesian Networks
     Learning: Forward–Backward Procedure
     Extended Forward–Backward Procedure
Model for Multiple Sensors Use


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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.