An Ant-Colony Optimization Clustering Model for Cellular Automata Routing in Wireless Sensor Networks

High efficient routing is an important issue for the design of wireless sensor network (WSN) protocols to meet the severe hardware and resource constraints. This paper presents an inclusive evolutionary reinforcement method. The proposed approach is a combination of Cellular Automata (CA) and Ant Colony Optimization (ACO) techniques in order to create collision-free trajectories for every agent of a team while their formation is kept unchallengeable. The method reacts with problem distribution changes and therefore can be used in dynamical or unknown environments, without the need of a priori knowledge of the space. The swarm of agents are divided into subgroups and all the desired trails are created with the combined use of a CA path finder and an ACO algorithm. In case of lack of pheromones, paths are created using the CA path finder. Compared to other methods, the proposed method can create accurate clustered, collision-free and reliable paths in real time with low complexity while the implemented system is completely autonomous.