Efficient Localization-based Clustering Approach for Increased Residual Energy and Enhanced Network Lifetime

Mobile Adhoc Networks are not without challenges such as limitations with scaling, and problems with excessive power consumptions which is mostly attributed to battery consumption. The movement of nodes causes an increasing use of energy, delay, and the shortened lifetime of nodes. A solution to these problems is a resource-efficient City Block-K Means Algorithm (CB-K Means) clustering method that prioritizes resource optimization and the lifespan of the network. These are localization, selection of Cluster Head (CH) and mobile node clustering. Mobile Node (MN) localization is performed using a Crossover and Mutation-based Jaya Optimization Algorithm (CM-JOA), followed by clustering through CB-K Means, where the CHs are selected with a linear scaling-based Satin Bowerbird Optimization Algorithm (LS-SBOA). The experimental results indicate that there is an improvement by 3.48% in the PDR (Packet Delivery Ratio), 7.3 percent packet loss, reduced clustering time of 3412ms and enhanced throughput of 889 bps.