Wireless sensor networks node localization via Leader Intelligent Selection optimization algorithm

In this paper, we propose a node localization algorithm based on the received signal strength (RSS) measurements and the Leader Intelligent Selection (LIS) optimization algorithm in Wireless Sensor Networks (WSN). The LIS optimization algorithm is proposed based on the idea of biological heuristic. By designing a simple animal group leader se lection mode, a leader candidates' group is searched by the leader searcher, and an optimal individual is selected from the group as the leader which is the global optimal solution of the optimization problem by evaluating each leader candidate's ability. In order to accelerate the leader's campaign and the evolutionary rate in the later period of LIS, the simple Minimum Mean Square Error (MMSE) algorithm or the centroid algorithm is adopted to obtain an initial coordinate as the initial leader of LIS algorithm using the information of the anchor node coordinates and the ranging findings. By considering fully the distance factor, an improved objective function is defined, so the node localization problem in WSN could be transformed into a nonlinear unconstrained optimization problem. The proposed LIS algorithm is used to solve this problem, and the obtained solution is the estimated value of the WSN node's coordinates. Compared with the Artificial Bee Colony (ABC) algorithm, the Particle Swarm Optimization (PSO) algorithm and the Genetic Algorithm (GA), the proposed LIS algorithm is better than the others in accuracy and calculation complexity.