When Compressive Sensing Meets Mobile Crowdsensing [electronic resource] /
Kong, Linghe.author.
Wang, Bowen.author.
Chen, Guihai.author.
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. To address these problematic aspects, compressive sensing, which works by accurately recovering a sparse signal using very few samples, has proven to offer an effective solution. .
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.
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. To address these problematic aspects, compressive sensing, which works by accurately recovering a sparse signal using very few samples, has proven to offer an effective solution. .
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.