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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.
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