A diversity-controllable genetic algorithm for optimal fused traffic planning on sensor networks

In some sensor network applications e.g. target tracing, multi-profile data about an event are fused at intermediate nodes. The optimal planning of such fused traffic is important for prolonging the network lifetime, because data communications consume the most energy of sensor networks. As a general method for such optimization problems, genetic algorithms suffer from tremendous communication diversities that increase greatly with the network size. In this paper, we propose a diversity-controllable genetic algorithm for optimizing fused traffic planning. Simulation shows that it gains remarkable improvements.