Distributed localization for anisotropic sensor networks using spatial clustering
ADCONS'11 Proceedings of the 2011 international conference on Advanced Computing, Networking and Security
Toward collinearity-aware and conflict-friendly localization for wireless sensor networks
Computer Communications
Robust localization against outliers in wireless sensor networks
ACM Transactions on Sensor Networks (TOSN)
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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 \schmi{ 0.4r} when the average sensor density is above eight, and the sensor localization accuracy thus is approximately better than \schmi{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.