Online Change Detection for Energy-Efficient Mobile Crowdsensing

Duc Viet Le Viet Duc; Johan Scholten; Paul J.M. Havinga

doi:10.1007/978-3-319-10359-4_1

Online Change Detection for Energy-Efficient Mobile Crowdsensing

Duc Viet Le Viet Duc, Johan Scholten, Paul J.M. Havinga

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

5 Citations (Scopus)

47 Downloads (Pure)

Abstract

Mobile crowdsensing is power hungry since it requires continuously and simultaneously sensing, processing and uploading fused data from various sensor types including motion sensors and environment sensors. Realizing that being able to pinpoint change points of contexts enables energy-efficient mobile crowdsensing, we modify histogram-based techniques to efficiently detect changes, which has less computational complexity and performs better than the conventional techniques. To evaluate our proposed technique, we conducted experiments on real audio databases comprising 200 sound tracks. We also compare our change detection with multivariate normal distribution and one-class support vector machine. The results show that our proposed technique is more practical for mobile crowdsensing. For example, we show that it is possible to save 80% resource compared to standard continuous sensing while remaining detection sensitivity above 95%. This work enables energy-efficient mobile crowdsensing applications by adapting to contexts.

Original language	English
Title of host publication	Mobile Web Information Systems
Subtitle of host publication	11th International Conference on Mobile Web and Information Systems, MobiWIS 2014
Editors	Irfan Awan, Muhammad Younas, Xavier Franch, Carme Quer
Place of Publication	London
Publisher	Springer
Pages	1-16
Number of pages	16
ISBN (Electronic)	978-3-319-10359-4
ISBN (Print)	978-3-319-10358-7
DOIs	https://doi.org/10.1007/978-3-319-10359-4_1
Publication status	Published - 27 Aug 2014
Event	11th International Conference on Mobile Web and Information Systems, MobiWIS 2014 - Barcelona, Spain Duration: 27 Aug 2014 → 29 Aug 2014 Conference number: 11

Publication series

Name	Lecture Notes in Computer Science
Publisher	Springer Verlag
Volume	8640
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	11th International Conference on Mobile Web and Information Systems, MobiWIS 2014
Abbreviated title	MobiWIS
Country/Territory	Spain
City	Barcelona
Period	27/08/14 → 29/08/14

Keywords

CAES-PS: Pervasive Systems
EWI-25022
Energy Efficiency
Resource Constraints
METIS-309573
Change Detection
Adaptive Sensing
Computational Complexity
Mobile Crowdsensing
IR-92420

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.1007/978-3-319-10359-4_1

changedetection_cameraready

Cite this

Le Viet Duc, D. V., Scholten, J., & Havinga, P. J. M. (2014). Online Change Detection for Energy-Efficient Mobile Crowdsensing. In I. Awan, M. Younas, X. Franch, & C. Quer (Eds.), Mobile Web Information Systems: 11th International Conference on Mobile Web and Information Systems, MobiWIS 2014 (pp. 1-16). (Lecture Notes in Computer Science; Vol. 8640). Springer. https://doi.org/10.1007/978-3-319-10359-4_1

Le Viet Duc, Duc Viet ; Scholten, Johan ; Havinga, Paul J.M. / Online Change Detection for Energy-Efficient Mobile Crowdsensing. Mobile Web Information Systems: 11th International Conference on Mobile Web and Information Systems, MobiWIS 2014. editor / Irfan Awan ; Muhammad Younas ; Xavier Franch ; Carme Quer. London : Springer, 2014. pp. 1-16 (Lecture Notes in Computer Science).

@inproceedings{e97381c660ba4773a0560a04ac3c8d6b,

title = "Online Change Detection for Energy-Efficient Mobile Crowdsensing",

abstract = "Mobile crowdsensing is power hungry since it requires continuously and simultaneously sensing, processing and uploading fused data from various sensor types including motion sensors and environment sensors. Realizing that being able to pinpoint change points of contexts enables energy-efficient mobile crowdsensing, we modify histogram-based techniques to efficiently detect changes, which has less computational complexity and performs better than the conventional techniques. To evaluate our proposed technique, we conducted experiments on real audio databases comprising 200 sound tracks. We also compare our change detection with multivariate normal distribution and one-class support vector machine. The results show that our proposed technique is more practical for mobile crowdsensing. For example, we show that it is possible to save 80% resource compared to standard continuous sensing while remaining detection sensitivity above 95%. This work enables energy-efficient mobile crowdsensing applications by adapting to contexts.",

keywords = "CAES-PS: Pervasive Systems, EWI-25022, Energy Efficiency, Resource Constraints, METIS-309573, Change Detection, Adaptive Sensing, Computational Complexity, Mobile Crowdsensing, IR-92420",

author = "{Le Viet Duc}, {Duc Viet} and Johan Scholten and Havinga, {Paul J.M.}",

note = "Best Paper Award; 11th International Conference on Mobile Web and Information Systems, MobiWIS 2014, MobiWIS ; Conference date: 27-08-2014 Through 29-08-2014",

year = "2014",

month = aug,

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doi = "10.1007/978-3-319-10359-4_1",

language = "English",

isbn = "978-3-319-10358-7",

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Le Viet Duc, DV, Scholten, J & Havinga, PJM 2014, Online Change Detection for Energy-Efficient Mobile Crowdsensing. in I Awan, M Younas, X Franch & C Quer (eds), Mobile Web Information Systems: 11th International Conference on Mobile Web and Information Systems, MobiWIS 2014. Lecture Notes in Computer Science, vol. 8640, Springer, London, pp. 1-16, 11th International Conference on Mobile Web and Information Systems, MobiWIS 2014, Barcelona, Spain, 27/08/14. https://doi.org/10.1007/978-3-319-10359-4_1

Online Change Detection for Energy-Efficient Mobile Crowdsensing. / Le Viet Duc, Duc Viet; Scholten, Johan; Havinga, Paul J.M.
Mobile Web Information Systems: 11th International Conference on Mobile Web and Information Systems, MobiWIS 2014. ed. / Irfan Awan; Muhammad Younas; Xavier Franch; Carme Quer. London: Springer, 2014. p. 1-16 (Lecture Notes in Computer Science; Vol. 8640).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

TY - GEN

T1 - Online Change Detection for Energy-Efficient Mobile Crowdsensing

AU - Le Viet Duc, Duc Viet

AU - Scholten, Johan

AU - Havinga, Paul J.M.

N1 - Conference code: 11

PY - 2014/8/27

Y1 - 2014/8/27

N2 - Mobile crowdsensing is power hungry since it requires continuously and simultaneously sensing, processing and uploading fused data from various sensor types including motion sensors and environment sensors. Realizing that being able to pinpoint change points of contexts enables energy-efficient mobile crowdsensing, we modify histogram-based techniques to efficiently detect changes, which has less computational complexity and performs better than the conventional techniques. To evaluate our proposed technique, we conducted experiments on real audio databases comprising 200 sound tracks. We also compare our change detection with multivariate normal distribution and one-class support vector machine. The results show that our proposed technique is more practical for mobile crowdsensing. For example, we show that it is possible to save 80% resource compared to standard continuous sensing while remaining detection sensitivity above 95%. This work enables energy-efficient mobile crowdsensing applications by adapting to contexts.

AB - Mobile crowdsensing is power hungry since it requires continuously and simultaneously sensing, processing and uploading fused data from various sensor types including motion sensors and environment sensors. Realizing that being able to pinpoint change points of contexts enables energy-efficient mobile crowdsensing, we modify histogram-based techniques to efficiently detect changes, which has less computational complexity and performs better than the conventional techniques. To evaluate our proposed technique, we conducted experiments on real audio databases comprising 200 sound tracks. We also compare our change detection with multivariate normal distribution and one-class support vector machine. The results show that our proposed technique is more practical for mobile crowdsensing. For example, we show that it is possible to save 80% resource compared to standard continuous sensing while remaining detection sensitivity above 95%. This work enables energy-efficient mobile crowdsensing applications by adapting to contexts.

KW - CAES-PS: Pervasive Systems

KW - EWI-25022

KW - Energy Efficiency

KW - Resource Constraints

KW - METIS-309573

KW - Change Detection

KW - Adaptive Sensing

KW - Computational Complexity

KW - Mobile Crowdsensing

KW - IR-92420

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DO - 10.1007/978-3-319-10359-4_1

M3 - Conference contribution

SN - 978-3-319-10358-7

T3 - Lecture Notes in Computer Science

SP - 1

EP - 16

BT - Mobile Web Information Systems

A2 - Awan, Irfan

A2 - Younas, Muhammad

A2 - Franch, Xavier

A2 - Quer, Carme

PB - Springer

CY - London

T2 - 11th International Conference on Mobile Web and Information Systems, MobiWIS 2014

Y2 - 27 August 2014 through 29 August 2014

ER -

Le Viet Duc DV, Scholten J, Havinga PJM. Online Change Detection for Energy-Efficient Mobile Crowdsensing. In Awan I, Younas M, Franch X, Quer C, editors, Mobile Web Information Systems: 11th International Conference on Mobile Web and Information Systems, MobiWIS 2014. London: Springer. 2014. p. 1-16. (Lecture Notes in Computer Science). doi: 10.1007/978-3-319-10359-4_1

Online Change Detection for Energy-Efficient Mobile Crowdsensing

Abstract

Publication series

Conference

Keywords

UN SDGs

Access to Document

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