eSENSE: Energy efficient stochastic sensing framework for wireless sensor platforms

Haiyang Liu, Abhishek Chandra, Jaideep Srivastava

Research output: Chapter in Book/Report/Conference proceedingConference contribution

19 Citations (Scopus)

Abstract

Energy is a precious resource in wireless sensor networks as sensor nodes are typically powered by batteries with high replacement cost. This paper presents eSENSE: an energy-efficient stochastic sensing framework for wireless sensor platforms. eSENSE is a node-level framework that utilizes knowledge of the underlying data streams as well as application data quality requirements to conserve energy on a sensor node. eSENSE employs a stochastic scheduling algorithm to dynamically control the operating modes of the sensor node components. This scheduling algorithm enables an adaptive sampling strategy that aggressively conserves power by adjusting sensing activity to the application requirements. Using experimental results obtained on Power-TOSSIM with a real-world data trace, we demonstrate that our approach reduces energy consumption by 29-36% while providing strong statistical guarantees on data quality.

Original languageEnglish
Title of host publicationProceedings of the Fifth International Conference on Information Processing in Sensor Networks, IPSN '06
Pages235-242
Number of pages8
Volume2006
DOIs
Publication statusPublished - 2006
Externally publishedYes
EventFifth International Conference on Information Processing in Sensor Networks, IPSN '06 - Nashville, TN
Duration: 19 Apr 200621 Apr 2006

Other

OtherFifth International Conference on Information Processing in Sensor Networks, IPSN '06
CityNashville, TN
Period19/4/0621/4/06

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Keywords

  • Energy management
  • Scheduling
  • Sensor networks

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Signal Processing
  • Electrical and Electronic Engineering

Cite this

Liu, H., Chandra, A., & Srivastava, J. (2006). eSENSE: Energy efficient stochastic sensing framework for wireless sensor platforms. In Proceedings of the Fifth International Conference on Information Processing in Sensor Networks, IPSN '06 (Vol. 2006, pp. 235-242) https://doi.org/10.1145/1127777.1127815