Soft Computing Based Cluster-Head Selection in Mobile Ad-Hoc Network
Jay Prakash, Deepak Kumar Gupt, Rakesh Kumar · Journal of Artificial Intelligence · 2017
Background and Objective: Mobile ad-hoc network (MANET) is a specific type of network that can be quickly deployed without any existing framework.Cluster formation and cluster head selection in MANET is an important issue in such networks.Clustering is one of the vital issues used in increasing the network life time by gathering the information from specific group of nodes and forwarding it to other neighbouring cluster heads.This paper propose a soft computing based approach for the selection of the cluster head in MANET.A cluster head selection model based on fuzzy logic has been devised.Cluster head selection is done on the basis of three parameters viz., residual energy, centrality and hop-count.Materials and Methods: The proposed approach has been implemented in MATLAB followed by execution of cluster-head selection based on fuzzy logic using 3 criteria viz; residual energy, hop count and centrality.Results: The benefits of this approach include reduced overhead, improved performance of cluster node selection and increased network lifetime.Conclusion: Through simulation, it has been observed that this approach outperforms over existing approaches.The model has also been analytically validated.The new important aspects can be integrating mobility and trust to the existing model as fourth parameter for cluster-head selection that helps in improving the network performance.