Distance estimation is vital for localization and many other applications in wireless sensor networks. In this paper, we 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. We 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. Under the log-normal model, we numerically study the bias and standard deviation associated with our method and show that for long distances our method outperforms the method based on received signal strength (RSS); we investigate the impact of the log-normal model uncertainty; we provide a Cramér-Rao lower bound (CRLB) analysis for the problem of estimating distances via connectivity and derive helpful guidelines for implementing our method. Finally, on applying the proposed method based on realistic measurement data and also in connectivity-based sensor localization, the superiority of the proposed method is confirmed.
In REF , a novel method is presented based on the MLE under a generic channel model, including the unit disk model and the more realistic lognormal model, and its error characteristics were analyzed in light of the CRLB.
Estimating distances via connectivity in wireless sensor networks
Wireless Communications and Mobile Computing
Wireless Communications and Mobile Computing
Computer Science