Scalable localization in wireless sensor networks

Authors:
Muralidhar Medidi;Roger A. Slaaen;Yuanyuan Zhou;Christopher J. Mallery;Sirisha Medidi
Affiliations:
School of Electrical Engineering and Computer Science, Washington State University, Pullman, WA;School of Electrical Engineering and Computer Science, Washington State University, Pullman, WA;School of Electrical Engineering and Computer Science, Washington State University, Pullman, WA;School of Electrical Engineering and Computer Science, Washington State University, Pullman, WA;School of Electrical Engineering and Computer Science, Washington State University, Pullman, WA
Venue:
HiPC'06 Proceedings of the 13th international conference on High Performance Computing
Year:
2006

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Abstract

Localization, an important challenge in wireless sensor networks, is the process of sensor nodes self-determining their position. The difficulty encountered is in cost-effectively providing acceptable accuracy in localization. The potential for the deployment of high density networks in the near future makes scalability a critical issue in localization. In this paper we propose Cluster-based Localization (CBL), which provides effective localization suitable for large and highly-dense networks. CBL utilizes both a computationally-intensive localization technique (non-metric multidimensional scaling (MDS)) and a less intensive trilateration to achieve balance between performance and cost. Clustering is utilized to select a subset of nodes to perform MDS and then extend their localization to the remaining network. Besides providing scalability clustering overcomes local irregularities and provides good accuracy even in irregular networks with or without obstacles. Simulation results illustrate that CBL reduces both computation and communication, while still yielding acceptable accuracy.