LAUSR

Wireless sensor networks are used for low-cost unsupervised observation in a wide-range of environments and their application is largely constrained by the limited power sources of their constituent sensor nodes. Techniques such as routing and clustering are promising and can extend network lifetime significantly, however finding an optimal routing and clustering configuration is a NP-hard problem. In this paper, we present an energy efficient binary particle swarm optimization based routing and clustering algorithm using an intuitive matrix-like particle representation. We propose a novel particle update strategy and an efficient linear transfer function which outperform previously employed particle update strategies and some traditional transfer functions. Detailed experiments confirmed that our routing and clustering algorithm yields significantly higher network lifetime in comparison to existing algorithms. Furthermore, our results suggest that Binary PSO is better equipped to solve discrete problems of routing and clustering than its continuous counterpart, PSO.