When Compressive Sensing Meets Mobile Crowdsensing

• Main Authors: Kong, Linghe (Author, http://id.loc.gov/vocabulary/relators/aut), Wang, Bowen (http://id.loc.gov/vocabulary/relators/aut), Chen, Guihai (http://id.loc.gov/vocabulary/relators/aut)
• Corporate Author: SpringerLink (Online service)
• Format: Electronic eBook
• Language: English
• Published: Singapore : Springer Singapore : Imprint: Springer, 2019.
• Edition: 1st ed. 2019.
• Subjects: Mobile computing.; Computer communication systems.; Data structures (Computer science).; Computers.; Mobile Computing.; Computer Communication Networks.; Data Structures and Information Theory.; Information Systems and Communication Service.

This book provides a comprehensive introduction to applying compressive sensing to improve data quality in the context of mobile crowdsensing. It addresses the following main topics: recovering missing data, efficiently collecting data, preserving user privacy, and detecting false data. Mobile crowd...

Table of Contents:

• Introduction
• Mathematical Theory of Compressive Sensing
• Basic Compressive Sensing for Data Reconstruction
• Bayesian Compressive Sensing for Task Allocation
• Adaptive Compressive Sensing for Incentive Mechanism
• Encoded Compressive Sensing for Privacy Preservation
• Iterative Compressive Sensing for Fault Detection
• Conclusion.