Energy-efficient cluster-head selection with fuzzy logic for robotic fish swarm

Abstract

Saving energy is an imperative problem in the design and operation of robotic fish swarm because each fish has limited power source and cannot be conveniently replaced in aquatic environment. A novel method is proposed for energy conservation. This is to make use of cluster structure to group the fish into clusters. Therefore, it is essential to elect part of robotic fish swarm to turn into cluster heads in order to group clusters though selecting these cluster heads is not easy due to the characteristics of robotic fish swarm. An energy-saving cluster head method with fuzzy logic is proposed, aiming to intelligently appoint cluster heads in robotic fish swarm for saving energy and extending the network lifetime. Three input linguistic variables are employed, which include residual energy of fish, fish mobility and position. The fish which has higher residual energy, less mobility and appropriate position will get more opportunity to turn into the cluster heads. Simulation results displays that this method can prolong the robotic fish swarm lifetime, reduce energy consumption, and outperform LEACH-M.