Irregular Cellular Learning Automata and Its Application to Clustering in Sensor Networks

In the first part of this paper, we propose a generalization of cellular learning automata (CLA) called irregular cellular learning automata (ICLA) which removes the restriction of rectangular grid structure in traditional CLA. This generalization is expected because there are a number of applications which cannot be adequately modeled with rectangular grids. One category of such applications is in the area of wireless sensor networks. In these networks, nodes are usually scattered randomly throughout the environment, so no regular structure can be assumed for modeling their behavior. In the second part of the paper, based on the proposed model we design a clustering algorithm for sensor networks. Simulation results show that the proposed clustering algorithm is very efficient and outperforms similar existing methods.