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portada Generic and Energy-Efficient Context-Aware Mobile Sensing
Type
Physical Book
Publisher
Language
Inglés
Pages
222
Format
Paperback
ISBN13
9781138894518
Edition No.
1

Generic and Energy-Efficient Context-Aware Mobile Sensing

Chi Harold Liu (Author) · Ozgur Yurur (Author) · CRC Press · Paperback

Generic and Energy-Efficient Context-Aware Mobile Sensing - Yurur, Ozgur ; Liu, Chi Harold

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Synopsis "Generic and Energy-Efficient Context-Aware Mobile Sensing"

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(R) 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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