A statistical geometry approach to distance estimation in wireless sensor networks

Abstract

Algorithmic approaches to the estimation of pairwise distances between the nodes of a wireless sensor network are highly attractive to provide information for routing and localization without requiring specific hardware to be added to cost/resource-constrained nodes. This paper exploits statistical geometry to derive robust estimators of the pairwise Euclidean distances from topological information typically available in any network. Extensive Monte Carlo experiments conducted on synthetic benchmarks demonstrate the improved quality of the proposed estimators with respect to the state of the art.