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Experimental Evaluation of a Simple Outlier RSSI Data Rejection Algorithm for Location Estimation in Wireless Sensor Networks

Daisuke ANZAI, Shinsuke HARA

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Summary :

The ability to estimate a target location is essential in many applications of wireless sensor networks. Received signal strength indicator (RSSI)-based maximum likelihood (ML) method in a wireless sensor network usually requires a pre-determined statistical model on the variation of RSSI in a sensing area and uses it as an ML function when estimating the location of a target in the sensing area. However, when estimating the location of a target, due to several reasons, we often measure the RSSIs which do not follow the statistical model, in other words, which are outlier on the statistical model. As the result, the effect of the outlier RSSI data worsens the estimation accuracy. If the wireless sensor network has a lot of sensor nodes, we can improve the estimation accuracy intentionally rejecting such outlier RSSIs. In this paper, we propose a simple outlier RSSI data rejection algorithm for an ML location estimation. The proposed algorithm iteratively eliminates the anchor nodes which measure outlier RSSIs. As compared with the location estimation methods with previously proposed outlier RSSI data rejection algorithms, our proposed method performs better with much less computational complexity.

Publication
IEICE TRANSACTIONS on Communications Vol.E91-B No.11 pp.3442-3449
Publication Date
2008/11/01
Publicized
Online ISSN
1745-1345
DOI
10.1093/ietcom/e91-b.11.3442
Type of Manuscript
Special Section PAPER (Special Section on Emerging Technologies for Practical Ubiquitous and Sensor Networks)
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