Generic and Energy-Efficient Context-Aware Mobile Sensing: 1st Edition (Hardback) book cover

Generic and Energy-Efficient Context-Aware Mobile Sensing

1st Edition

By Ozgur Yurur, Chi Harold Liu

CRC Press

221 pages | 41 B/W Illus.

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Hardback: 9781498700108
pub: 2015-02-02
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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.

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




About the Authors

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 Liuis 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.

Subject Categories

BISAC Subject Codes/Headings:
COMPUTERS / Networking / General
COMPUTERS / Software Development & Engineering / Systems Analysis & Design