Robust, probabilistic, constraint-based localization for wireless sensor networks

Localization is a fundamental service for many applications in wireless sensor networks. In this paper we propose a probabilistic, constraint-based approach robust to range measurement inaccuracies. The proposed approach pro- ceeds in three phases: the first phase involves modeling the uncertainties of range measurements; in the second phase, a set of probabilistic constraints are computed and combined to produce initial position estimates; in the final phase, negative constraints are used to refine the initial estimates. We evaluated the proposed approach through simulations based on real-world measurements; the results are compared with two other local- ization schemes and the Cramer-Rao lower bound. The results show that, for inaccurate range measurements, the proposed probabilistic approach performs the best and close to the optimal bound.

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