Locally Centralized SOCP-based Localization Technique for Wireless Sensor Network

AbstractNode localization is one of the essential requirements to most applications of wireless sensor networks. This paper proposes a clustered localization approach for WSNs based on Second Order Cone Programming (SOCP). The cluster solves the SOCP problem as a global minimization to get positions of the cluster sensor nodes. To enhance localization accuracy, a cluster level refinement step is implemented using Gauss-Newton optimization. The initial position for the Gauss-Newton optimization is the position drawn from the preprocessor SOCP. The proposed approach scales well for large networks and provides a considerable reduction in computation time while yielding good localization accuracy