Topology Control Scheduling: Based on the Distributed Learning Automata

This paper, presents a learning automata based algorithm (LABTA) to determine an appropriate time interval for topology control, while capturing network updates and minimizing algorithm overhead. This algorithm is application dependent and it is possible to consider other parameters like delay, etc. In this paper, we describe how to merge parameters and compute the fitness. We have implemented the proposed algorithm with both of standard and distributed models of the learning automata. Simulation results show that our proposed algorithm outperforms periodic topology control. In addition, a comparison can be also found between two most well known topology control protocols; LMST and K-Neigh.