3D Localization Using Social Network Analysis for Wireless Sensor Networks

Autonomous localization of nodes is essential in wireless sensor networks (WSNs). Various methods have been proposed on localization techniques and algorithm to improve the localization accuracy. Received signal strength indicator (RSSI) is a well-known parameter widely used for localization in many areas application scenarios. This paper presents a novel idea based on Social Network Analysis which is a new trend that deals with relationships between nodes in any network with different metrics. We use closeness centrality (CC) to represent the node importance in a network latter elected as a cluster head. The highest CC value with the shortest path can form a cluster for a node awaiting for localization. The cluster head then selects four anchors to form a trilateration based on RSSI values. The formulation of clusters is close to the unknown node, and the proposed scheme provides better localization accuracy as compared to existing RSSI techniques. Simulation results show that the proposed scheme can an average localization error of 0.32m with a standard variation of 0.26 m.