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Probabilistic Estimation Algorithm for Cooperative Localization in Wireless Sensor Networks
M. Castillo-Effen, M.A. Labrador, W.A. Moreno and K.P.Valavanis

Localization is a key function in Wireless Sensor Networks (WSNs). Many applications and internal mechanisms require nodes to know their location. This work proposes a new sequential estimation algorithm for distributed cooperative localization, whose simplicity makes it amenable to self-localization in WSNs, characterized by their restricted computational and energy resources. The algorithm is inspired in sequential Monte-Carlo estimation techniques, viz. particle filters, that excel in robustness and simplicity for estimation applications. However, particle filters require significant amounts of memory and computational power for managing large numbers of particles. The presented technique reduces the number of particles and offers a solution that can be adapted to different applications according to the required accuracy and the constraints of the platform in use.

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