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Coverage for target localization in wireless sensor networks
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Source Information Processing In Sensor Networks archive
Proceedings of the 5th international conference on Information processing in sensor networks table of contents
Nashville, Tennessee, USA
SESSION: Main track--wireless sensor networking table of contents
Pages: 118 - 125  
Year of Publication: 2006
ISBN:1-59593-334-4
Authors
Wei Wang  National University of Singapore
Vikram Srinivasan  National University of Singapore
Bang Wang  National University of Singapore
Kee-Chaing Chua  National University of Singapore
Sponsor
ACM: Association for Computing Machinery
Publisher
ACM  New York, NY, USA
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ABSTRACT

Target tracking and localization are important applications in wireless sensor networks. Although the coverage problem for target detection has been intensively studied, few consider the coverage problem from the perspective of target localization. In this paper, we propose two methods to estimate the necessary sensor density which can guarantee a localization error bound over the sensing field. In the first method, we convert the coverage problem for localization to a conventional disk coverage problem, where the sensing area is a disk centered around the sensor. Our results show that the disk coverage model requires 4 times more sensors for tracking compared to detection applications. We then introduce the idea of sector coverage, which can satisfy the same coverage conditions with 2 times less sensors over the disk coverage approach. This shows that conventional disk coverage model is insufficient for tracking applications, since it overestimates the sensor density by two times. Simulation results show that the network density requirements derived through sector coverage are close to the actual need for target tracking applications.


REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

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Collaborative Colleagues:
Wei Wang: colleagues
Vikram Srinivasan: colleagues
Bang Wang: colleagues
Kee-Chaing Chua: colleagues