Mobility-based Multicast Routing Algorithm in Wireless Mobile Ad-Hoc Networks: A Learning Automata Approach

In this paper, we propose a distributed learning automata-based algorithm to solve the multicast routing problem in wireless mobile ad-hoc networks. The proposed algorithm estimates the expected relative mobility of each host, by sampling its movement parameters in various epochs, to realistically predict its motion behavior, and takes advantage of the Steiner connected dominating set to form the virtual multicast backbone. To do this, a stochastic version of the minimum Steiner connected dominating set problem in weighted network graphs, where the relative mobility of each host is considered as its weight is introduced. Then, a distributed learning automata-based algorithm is designed to solve this problem. The designed algorithm is proposed for multicast routing in wireless mobile ad-hoc networks. The experiments show the superiority of the proposed multicast routing algorithm over the existing methods in terms of the packet delivery ratio, multicast route lifetime, and end-to-end delay. We present a strong convergence theorem in which the convergence of the proposed distributed learning automata-based algorithm to the optimal solution is proved. It is shown that the most stable multicast route is found with a probability as close as to unity by the proper choice of the parameters of the distributed learning automata. Keyword: Wireless mobile ad-hoc networks, multicast routing, Steiner connected dominating set, distributed learning automata