Scalable SOCP-based localization technique for wireless sensor network

Node localization is one of the essential requirements to most applications of wireless sensor networks. This paper presents a detailed implementation of a centralized localization technique for WSNs based on Second Order Cone Programming (SOCP). To allow scalability, it also proposes a clustered localization approach for WSNs based on that centralized SOCP technique. The cluster solves the SOCP problem as a global minimization problem 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 localization. The proposed approach scales well for large networks and provides a considerable reduction in computation time while yielding good localization accuracy.