Wireless sensor network localization with connectivity-based refinement using mass spring and Kalman filtering

Abstract

Since many range-free localization algorithms depend on only a few anchors and implicit range estimations, they produce poor results. In this article, we propose a distributed range-free algorithm to improve localization accuracy by using one-hop neighbors as well as anchors. When an unknown node knows which nodes it can directly communicate with, but does not know how far they are exactly placed, the node should have a location having the average distance to all neighbors since the location minimizes the sum of squares of hop distance errors. In the proposed algorithm, each node initializes its location using the information of anchors and updates it based on mass spring method and Kalman filtering with the location estimates of one-hop neighbors until the equilibrium is achieved. Subsequently, the network has the shape of isotropic graph with minimized variance of links between one-hop neighbors. We evaluate our algorithm and compare it with other range-free algorithms through simulations under varying node density, anchor ratio, and node deployment method.