Evolutionary based path-finding for mobile agents in sensor networks

In this thesis, we present Evolutionary based path finding approach that can be applied to multi-agent based Wireless Sensor Network (WSN) to improve their performance. Path finding for mobile agents in sensor networks is a challenging problem, the objective is to make the agents follow a specified path and return to the collection point with the data collected from each sensor node. This thesis proposes a genetic algorithm that plans the simultaneous paths that intelligent mobile agents are to follow, such that the sensed information is collected within a time bound, and the energy spent is minimized. The simulations provide empirical results that illustrate the effective functioning of our approach under a variety of different topologies and assumptions. This thesis also discusses the useful insights which will play an important role in designing future extensions to this basic approach.