A bio-inspired algorithm for performance optimization in wireless sensor networks

A wireless sensor network typically comprises a number of inexpensive power constrained sensors which collect data from the sensing environment and transmit them towards the base station in a coordinated way. Employing techniques of clustering and election of cluster heads can increase the transmission efficiency and prolong the network lifetime. This paper proposes a bio-inspired de-centralized clustering algorithm for wireless sensor networks. The clustering algorithm is evaluated assuming a first-order radio model. Simulation results show that the proposed algorithm brings a 16 % to 161 % improvement over other de-centralized clustering algorithms in terms of network lifetime. Simulation results also show that the proposed de-centralized clustering algorithm has a similar performance as the centralized clustering algorithm.