Refining hop-count for localisation in wireless sensor networks

Abstract: Distance estimation is a crucial component in localisation for wireless sensor networks. Among the estimation methods, hop-count is widely used in situations where only connectivity information is available. However, hop-count is integer-valued, implying crude distance estimation. In this paper, we refine hop-count to achieve better distance estimation. This is done by estimating neighbour distance and then approximating non-neighbour distance by the length of the shortest path. To estimate neighbour distance, we propose three estimators and show that they have negligible bias. We also show that the variance of the estimators is related to node density. The final refined hop-counts are further studied by simulations. Results verify the improvement on distance estimation and show that existing localisation methods can benefit from the improvement in various scenarios