A Cooperative Localization Algorithm with Cluster Nodes Selection Based on Cramer-Rao Lower Bound

Cooperative localization has become a promising solution for location-enabled technologies in Wireless Sensor Networks (WSNs). However, it suffers from great energy consumption problem due to the energy-constrained characteristic of the networks. To alleviate this problem, we propose a cluster nodes selection strategy based on the Cramer-Rao lower bound (CRLB) for the cooperative localization algorithm in WSN. We first define clusters for every agent node by setting the received signal strength (RSS) threshold to screen out some less useful nodes, which greatly saves the energy at a cost of only a slight degradation in accuracy. Then, to improve the localization accuracy, the cluster nodes selection strategy catches the nodes that make the biggest contribution to localization results while discarding the least ones based on the derived analogous-CRLB values. Simulations show that the number of nodes participating in the localization is greatly decreased, which means a substantial reduction in energy consumption. In addition, the localization mean absolute error performance is significantly improved by using the proposed nodes selection algorithm.