Multicast Tree Computation for Group Communication in Mobile Networks using Optimization Techniques

Abstract

Modern group communication based applications require multiple parameters to be considered for routing in a Cellular network. Traditional algorithms fail in the situations where these parameters frequently change due to the dynamism prevailing in the network. A new technique for topology discovery in these types of networks using ant colony optimization (ACO) has been proposed based on the restricted flooding principle. To provide a better quality of service in routing with multiple constraints, a genetic algorithm based routing has been proposed to find optimal routes within a shorter span of time than the traditional deterministic routing algorithms. Moreover, with the exponential growth in the number of mobile users, to enable a large number of users to participate in a group communication, a parallel genetic algorithm (GA) is proposed in this paper. Our simulation results show that the topology discovery using ant colony optimization is faster. The Call service rate using parallel genetic algorithm is more than that of sequential genetic algorithm and the Call blocking rate of parallel genetic algorithm is less than that of sequential genetic algorithm, for large number of routers in the network.