Multihop Range-Free Localization in Anisotropic Wireless Sensor Networks: A Pattern-Driven Scheme

This paper focuses on multihop range-free localization in anisotropic wireless sensor networks. In anisotropic networks, geometric distance between a pair of sensor nodes is not always proportional to their hop count distance, which undermines the assumption of many existing range-free localization algorithms. To tolerate network anisotropy, we propose a pattern-driven localization scheme, which is inspired by the observation that in an anisotropic network the hop count field propagated from an anchor exhibits multiple patterns, under the interference of multiple anisotropic factors. Our localization scheme therefore for different patterns adopts different anchor-sensor distance estimation algorithms. The average anchor-sensor distance estimation accuracy of our scheme, as demonstrated by both theoretical analysis and extensive simulations, is improved to be better than 0.4r when the average sensor density is above eight, and the sensor localization accuracy thus is approximately better than 0.5r. This localization accuracy can satisfy the needs of many location-dependent protocols and applications, including geographical routing and tracking. Compared with previous localization algorithms that declares to tolerate network anisotropy, our localization scheme excels in 1) higher accuracy stemming from its ability to tolerate multiple anisotropic factors, including the existence of obstacles, sparse and nonuniform sensor distribution, irregular radio propagation pattern, and anisotropic terrain condition, 2) localization accuracy guaranteed by theoretical analysis, rather than merely by simulations, and 3) a distributed solution with less communication overhead and enhanced robustness to different network topologies.