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When compressive sensing meets mobil...

Kong, Linghe.

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When compressive sensing meets mobile crowdsensing

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	When compressive sensing meets mobile crowdsensing/ by Linghe Kong, Bowen Wang, Guihai Chen.
作者:	Kong, Linghe.
其他作者:	Wang, Bowen.
出版者:	Singapore :Springer Singapore : : 2019.,
面頁冊數:	xii, 127 p. :ill., digital ;24 cm.
內容註:	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.
Contained By:	Springer eBooks
標題:	Sensor networks. -
電子資源:	https://doi.org/10.1007/978-981-13-7776-1
ISBN:	9789811377761

When compressive sensing meets mobile crowdsensing
Kong, Linghe.

When compressive sensing meets mobile crowdsensing[electronic resource] /by Linghe Kong, Bowen Wang, Guihai Chen. - Singapore :Springer Singapore :2019. - xii, 127 p. :ill., digital ;24 cm.

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.

ISBN: 9789811377761

Standard No.: 10.1007/978-981-13-7776-1doiSubjects--Topical Terms:

581965
Sensor networks.

LC Class. No.: TK7872.D48

Dewey Class. No.: 681.2

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505 0 $a 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.
520 $a 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.
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