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Generic and Energy-Efficient Context-Aware Mobile Sensing
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Table of Contents

Context Awareness for Mobile Sensing
Introduction
Context Awareness Essentials
Contextual Information
Context Representation
ContextModeling
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
Discussions
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
Discussions
Proposed 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 Problem
Simulations
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 Evaluation

Energy Efficiency in Physical Hardware
Discussions
Battery Modeling
Modeling of Energy Consumption by Sensors
Preliminaries
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
Evaluation
Inference
Learning: Forward-Backward Procedure
Extended Forward-Backward Procedure
Model for Multiple Sensors Use

Appendix
References
Index

About the Author

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.

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