When Compressive Sensing Meets Mobile Crowdsensing

Kong, Linghe; Wang, Bowen; Chen, Guihai

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 crowdsensing, as an emerging sensing paradigm, enables the masses to take part in data collection tasks with the aid of powerful mobile devices. However, mobile crowdsensing platforms have yet to be widely adopted in practice, the major concern being the quality of the data collected. There are numerous causes: some locations may generate redundant data, while others may not be covered at all, since the participants are rarely systematically coordinated; privacy is a concern for some people, who don’t wish to share their real-time locations, and therefore some key information may be missing; further, some participants may upload fake data in order to fraudulently gain rewards. Toaddress these problematic aspects, compressive sensing, which works by accurately recovering a sparse signal using very few samples, has proven to offer an effective solution.

Details

Published by: Springer

Publication Date: 2020-08-14

Format: Paperback

ISBN-13: 9789811377785

DOI: 10.1007/978-981-13-7776-1

Dimensions: 235cm x155cm

Pages: 127