An Enhanced GSO Technique for Wireless Sensor Networks Optimization

Sensor networks are an emerging field of research which combines many challenges of modern computer science, wireless communication and mobile computing. They present significant systems challenges involving the use of large numbers of resource-constrained nodes operating essentially unattended and exposed to potential local communication failures. The physical constraints of a sensor network, especially in terms of energy, are an intrinsically complex problem and request to take into account many parameters at the same time; in this paper we investigate the possibility of using evolutionary algorithms to optimize the lifetime of a network with a limited power supply. The Genetical Swarm Optimization (GSO) is a recently introduced hybrid technique between GA and PSO. It has developed in order to exploit in the most effective way the uniqueness and peculiarities of these classical optimization approaches, and it can be used to solve combinatorial optimization problems. In this paper the authors present an enhancement of this technique for application in the maximization of the lifetime a wireless sensor network.