Location discovery in Wireless Sensor Networks using metaheuristics

Abstract

Wireless Sensor Networks (WSN) monitor the physical world using small wireless devices known as sensor nodes, with high precision and in real time, without the intervention of a human operator. Location information plays a critical role in many of the applications where WSN are used. Though a simple and effective solution could be to equip every node with self-locating hardware such as a GPS, the resulting cost renders such a solution unefficient. A widely used self-locating mechanism consists in equipping a small subset of the nodes with some GPS-like hardware, while the rest of the nodes employ reference estimations (received signal strength, time-of-arrival, etc.) in order to determine their locations. The task of determining the node locations using node-to-node distances combined with a set of known node locations is referred to as location discovery (LD). The main difficulty found in LD is the presence of distance estimation errors, which result in node positioning errors. We describe in this work an error model for the estimations, and propose a two-stage search procedure that combines minimization of an error norm function with maximization of a maximum likelihood function to solve the problem. We perform an empirical study of the performance of several variants of the guiding functions, and several metaheuristics used to solve real LD problem instances. Finally, we test our proposed technique against the single phase techniques in order to evaluate its performance.