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Poster: Reducing power consumption of human activity sensing using compressed sensing

Published: 01 November 2011 Publication History

Abstract

Almost all recent mobile phones are equipped with multiple sensors, such as cameras, GPS, and accelerometers. By exploiting the sensing features, we capture many different events and share them over the mobile network. One of the most important challenges for such a participatory sensing system is the reduction of the battery consumption of the mobile device because the sensing task usually runs as a secondary task and should not disrupt the primary tasks of the mobile phone, such as phone calls. We overcome this problem by compressing the sensed data and sending a minimum amount of data over the wireless link. The compressed sensing (CS) technique is used for this compression with simple matrix operations at the mobile side, and the CPU-intensive reconstruction is performed on the resource-rich machine on the network side. In this paper, we validate this idea by implementing it on the iPhone/iPod platform. Since CS is a lossy compression technique, the reconstructed signal contains errors depending on the degree of sparseness of the original signal. We show that our system can reduce power consumption by approximately 16% compared with ZIP compression, and evaluate the reconstruction error using real sensing data of 86 test subjects.

References

[1]
E. J. Candés and M. B. Wakin, "An Introduction To Compressive Sampling," IEEE, Signal Processing Magazine, Vol. 25, no.2, pp.21--30, March 2008.
[2]
S. Yang and M. Gerla: "Energy-Efficient Accelerometer Data Transfer for Human Body Movement Studies," IEEE SUTC, pp.304--311, California, USA, July 2010.
[3]
N. Kawaguchi et al., "HASC Challenge: Gathering Large Scale Human Activity Corpus for the Real-World Activity Understandings," Proceeding of AH'11, Article No.: 27, March 2011.

Cited By

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  • (2013)Pattern-based compressed phone sensing2013 IEEE Global Conference on Signal and Information Processing10.1109/GlobalSIP.2013.6736842(169-172)Online publication date: Dec-2013

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  1. Poster: Reducing power consumption of human activity sensing using compressed sensing

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      cover image ACM Conferences
      SenSys '11: Proceedings of the 9th ACM Conference on Embedded Networked Sensor Systems
      November 2011
      452 pages
      ISBN:9781450307185
      DOI:10.1145/2070942

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      Association for Computing Machinery

      New York, NY, United States

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      Published: 01 November 2011

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      Author Tags

      1. accelerometer
      2. compressed sensing
      3. context

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      Overall Acceptance Rate 174 of 867 submissions, 20%

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      • (2013)Pattern-based compressed phone sensing2013 IEEE Global Conference on Signal and Information Processing10.1109/GlobalSIP.2013.6736842(169-172)Online publication date: Dec-2013

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