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Distance estimation is vital for localization and many other applications in wireless sensor networks. In this paper, the authors develop a method that employs a Maximum-Likelihood Estimator (MLE) to estimate distances between a pair of neighboring nodes in static wireless sensor networks using their local connectivity information, namely the numbers of their common and non-common one-hop neighbors. They present the distance estimation method under a generic channel model, including the unit disk (communication) model and the more realistic log-normal (shadowing) model as special cases.
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