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

Authors:
Yantao Pan;Xicheng Lu;Peidong Zhu;Shen Ma
Affiliations:
School of Computer, National University of Defense Technology, Changsha, P.R. China;School of Computer, National University of Defense Technology, Changsha, P.R. China;School of Computer, National University of Defense Technology, Changsha, P.R. China;School of Computer, National University of Defense Technology, Changsha, P.R. China
Venue:
ACSAC'06 Proceedings of the 11th Asia-Pacific conference on Advances in Computer Systems Architecture
Year:
2006

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Abstract

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.