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25022 Online Change Detection for Energy-Efficient Mobile Crowdsensing
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Le, Viet Duc and Scholten, J. and Havinga, P.J.M. (2014) Online Change Detection for Energy-Efficient Mobile Crowdsensing. In: 11th International Conference on Mobile Web and Information Systems, MobiWIS 2014, 27-29 Aug 2014, Barcelona, Spain. pp. 1-16. Lecture Notes in Computer Science 8640. Springer Verlag. ISSN 0302-9743 ISBN 978-3-319-10358-7

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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.

Item Type:Conference or Workshop Paper (Full Paper, Talk)
Research Group:EWI-PS: Pervasive Systems
Research Program:CTIT-WiSe: Wireless and Sensor Systems
Research Project:COMMIT/SENSA: Sensor Networks for Public Safety
Additional Information:Best Paper Award
Uncontrolled Keywords:Mobile Crowdsensing, Adaptive Sensing, Change Detection
ID Code:25022
Deposited On:10 November 2014
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