國立高雄大學圖資館 |

Language: English

Back

When compressive sensing meets mobil...

Chen, Guihai.

When compressive sensing meets mobile crowdsensing

Record Type:	Electronic resources : Monograph/item
Title/Author:	When compressive sensing meets mobile crowdsensingby 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.
Contained By:	Springer eBooks
Subject:	Sensor networks.
Online resource:	https://doi.org/10.1007/978-981-13-7776-1
ISBN:	9789811377761$q(electronic bk.)

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$q(electronic bk.)

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

214195
Sensor networks.

LC Class. No.: TK7872.D48

Dewey Class. No.: 681.2

When compressive sensing meets mobile crowdsensing
LDR:02478nmm a2200325 a 4500 001 562687
003 DE-He213
005 20190608191532.0
006 m d
007 cr nn 008maaau
008 200227s2019 si s 0 eng d
020 $a 9789811377761$q(electronic bk.)
020 $a 9789811377754$q(paper)
024 7 $a 10.1007/978-981-13-7776-1 $2 doi
035 $a 978-981-13-7776-1
040 $a GP $c GP
041 0 $a eng
050 4 $a TK7872.D48
072 7 $a UMS $2 bicssc
072 7 $a COM051460 $2 bisacsh
072 7 $a UMS $2 thema
082 0 4 $a 681.2 $2 23
090 $a TK7872.D48 $b K82 2019
100 1 $a Kong, Linghe. $3 847920
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 214195
650 0 $a Multisensor data fusion. $3 182455
650 0 $a Data mining. $3 184440
650 0 $a Mobile communication systems. $3 182488
650 1 4 $a Mobile Computing. $3 763350
650 2 4 $a Computer Communication Networks. $3 218087
650 2 4 $a Data Structures and Information Theory. $3 825714
650 2 4 $a Information Systems and Communication Service. $3 274025
700 1 $a Wang, Bowen. $3 847921
700 1 $a Chen, Guihai. $3 285319
710 2 $a SpringerLink (Online service) $3 273601
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)