Adaptive cluster formation in MANET using particle swarm optimization

Abstract

“Mobile Ad Hoc Network” (MANET) is a form of ad hoc network in which the devices are dynamic and configure themselves during the fly. Hence, for an energy efficient and stable communication to be established among the nodes, the nodes must be organized into groups called clusters. Associativity is one among the good metrics that can configure the speed of a node with respect to its neighbour. In the proposed work, along with associativity, residual energy and nodes degree are the multiple metrics that are been considered to elect the clusterheads. Optimal clustering of nodes in dynamic MANET is an NP-Hard problem that can be solved by tools that include evolutionary computation, swarm optimization, etc. Among the swarm optimization methods, genetic algorithm (GA) and particle swarm optimization (PSO) techniques were been proved to be the efficient and promising methodologies for solving the optimization problems. These techniques are finding their popularity in wide range due to their adaptable nature and ability to optimize even large complex search spaces applied to non-differentiable cost functions. Hence, the main objective of the proposed work is to select stable clusterhead by considering multiple metrics that are used to frame the fitness function for GA and PSO. Since the proposed work includes energy as one of the metrics, it can be used in distributed MANET environment with nodes having different energy levels. Since, the associativity of nodes reflects the stability with their neighbours, the proposed system can also be used in dynamic and static environment. The proposed work has been simulated with both GA and PSO, and the results have shown that PSO resulted with better performance compared to GA, in terms of clusterhead count under various simulation environments.