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When compressive sensing meets mobil...

Kong, Linghe.

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When compressive sensing meets mobile crowdsensing

Record Type:	Electronic resources : Monograph/item
Title/Author:	When compressive sensing meets mobile crowdsensing/ by Linghe Kong, Bowen Wang, Guihai Chen.
Author:	Kong, Linghe.
other author:	Wang, Bowen.
Published:	Singapore :Springer Singapore : : 2019.,
Description:	xii, 127 p. :ill., digital ;24 cm.
[NT 15003449]:	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
Subject:	Sensor networks. -
Online resource:	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

When compressive sensing meets mobile crowdsensing
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100 1 $a Kong, Linghe. $3 3411809
245 1 0 $a When compressive sensing meets mobile crowdsensing $h [electronic resource] / $c by Linghe Kong, Bowen Wang, Guihai Chen.
260 $a Singapore : $b Springer Singapore : $b Imprint: Springer, $c 2019.
300 $a xii, 127 p. : $b ill., digital ; $c 24 cm.
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.
650 0 $a Sensor networks. $3 581965
650 0 $a Multisensor data fusion. $3 581966
650 0 $a Data mining. $3 562972
650 0 $a Mobile communication systems. $3 567564
650 1 4 $a Mobile Computing. $3 3201332
650 2 4 $a Computer Communication Networks. $3 775497
650 2 4 $a Data Structures and Information Theory. $3 3382368
650 2 4 $a Information Systems and Communication Service. $3 891044
700 1 $a Wang, Bowen. $3 3411810
700 1 $a Chen, Guihai. $3 893614
710 2 $a SpringerLink (Online service) $3 836513
773 0 $t Springer eBooks
856 4 0 $u https://doi.org/10.1007/978-981-13-7776-1
950 $a Computer Science (Springer-11645)