On energy-efficient trap coverage in wireless sensor networks

Junkun Li, Jiming Chen, Shibo He, Tian He, Yu Gu, Youxian Sun

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

11 Scopus citations

Abstract

In wireless sensor networks (WSNs), trap coverage has recently been proposed to tradeoff between the availability of sensor nodes and sensing performance. It offers an efficient framework to tackle the challenge of limited resources in large scale sensor networks. Currently, existing works only studied the theoretical foundation of how to decide the deployment density of sensors to ensure the desired degree of trap coverage. However, the practical issues such as how to efficiently schedule sensor node to guarantee trap coverage under an arbitrary deployment is still left untouched. In this paper, we formally formulate the Minimum Weight Trap Cover Problem and prove it is an NP-hard problem. To solve the problem, we introduce a bounded approximation algorithm, called Trap Cover Optimization (TCO) to schedule the activation of sensors while satisfying specified trap coverage requirement. The performance of MinimumWeight Trap Coverage we find is proved to be at most O(ρ) times of the optimal solution, where ρ is the density of sensor nodes in the region. To evaluate our design, we perform extensive simulations to demonstrate the effectiveness of our proposed algorithm and show that our algorithm achieves at least 14% better energy efficiency than the state-of-the-art solution.

Original languageEnglish (US)
Title of host publicationProceedings - 2011 32nd IEEE Real-Time Systems Symposium, RTSS 2011
Pages139-148
Number of pages10
DOIs
StatePublished - Dec 1 2011
Event2011 32nd IEEE Real-Time Systems Symposium, RTSS 2011 - Vienna, Austria
Duration: Nov 29 2011Dec 2 2011

Publication series

NameProceedings - Real-Time Systems Symposium
ISSN (Print)1052-8725

Other

Other2011 32nd IEEE Real-Time Systems Symposium, RTSS 2011
CountryAustria
CityVienna
Period11/29/1112/2/11

Keywords

  • Energy-efficient
  • Scheduling
  • Trap coverage
  • Wireless sensor networks

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