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Issue No. 11 - November (2011 vol. 22)
ISSN: 1045-9219
pp: 1943-1951
Ziguo Zhong , University of Minnesota, Minneapolis
Tian He , University of Minnesota, Minneapolis
Wireless sensor networks have been considered as a promising tool for many location-dependent applications. In such deployments, the requirement of low system cost prohibits many range-based methods for sensor node localization; on the other hand, range-free approaches depending only on radio connectivity may underutilize the proximity information embedded in neighborhood sensing. In response to these limitations, this paper introduces a proximity metric called RSD to capture the distance relationships among 1-hop neighboring nodes in a range-free manner. With little overhead, RSD can be conveniently applied as a transparent supporting layer for state-of-the-art connectivity-based localization solutions to achieve better accuracy. We implemented RSD with three well-known algorithms and evaluated using two outdoor test beds: an 850-foot-long linear network with 54 MICAz motes, and a regular 2D network covering an area of 10,000 square feet with 49 motes. Results show that our design helps eliminate estimation ambiguity with a subhop resolution, and reduces localization errors by as much as 35 percent. In addition, simulations confirm its effectiveness for large-scale networks and reveal an interesting feature of robustness under unevenly distributed radio path loss.
Wireless sensor networks, localization, range free, neighborhood sensing, signature distance, RSD.

Z. Zhong and T. He, "RSD: A Metric for Achieving Range-Free Localization beyond Connectivity," in IEEE Transactions on Parallel & Distributed Systems, vol. 22, no. , pp. 1943-1951, 2011.
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