Self-Organization of Sensor Networks Using Genetic Algorithms

In this paper we propose a reduced-complexity genetic algorithm for optimization of multi-hop sensor networks. The goal of the system is to generate optimal number of sensor-clusters with cluster-heads. It results in minimization of the power-consumption of the sensor-system while maximizing the sensor objectives (coverage and exposure). The genetic algorithm is used to adaptively create various components such as cluster-members, cluster-heads, and next-cluster. These components are then used to evaluate the average fitness of the system based on the sequence of communication links towards the sink.