Generic and Energy-Efficient Context-Aware Mobile Sensing

Ozgur Yurur, Chi Harold Liu

  • 出版商: CRC
  • 出版日期: 2023-12-31
  • 售價: $2,760
  • 貴賓價: 9.5$2,622
  • 語言: 英文
  • 裝訂: Paperback
  • ISBN: 1138894516
  • ISBN-13: 9781138894518
  • 尚未上市,歡迎預購


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.