Date(s) - 30/11/2017
4:00 pm - 5:30 pm
Abstract: Clustering makes an ad hoc network scalable while increasing control overhead. In this paper, we propose Random Clustering Algorithm that is a simple and efficient clustering algorithm with minimal overhead. In this algorithm, cluster heads are determined randomly in a distributed fashion. An analytic model is introduced for nodes to compute the probability of declaring themselves as cluster heads. We validate the analytic model by Monte-Carlo simulations. Furthermore, we propose a cross-layer clustered stack and simulate simple applications in stationary and dynamic topologies using OMNeT++. Discrete event simulation results show that Random Clustering Algorithm eliminates a significant amount of control overhead and the performance of the algorithm is considerably better compared to its opponent, identity-based clustering.