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RSD: A Metric for Achieving Range-Free Localization beyond Connectivity

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2 Author(s)
Ziguo Zhong ; Dept. of Comput. Sci. & Eng., Univ. of Minnesota, Minneapolis, MN, USA ; Tian He

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.

Published in:

IEEE Transactions on Parallel and Distributed Systems  (Volume:22 ,  Issue: 11 )