Adaptive Clustering Algorithm for IIoT Based Mobile Opportunistic Networks

Abstract

The clustering algorithms play a crucial role for energy saving solutions in mobile opportunistic networks. If the selection of cluster head is made appropriately, then the energy can be consumed optimally. The existing clustering algorithms do not consider the optimal selection of the cluster head resulting in low survival rates and high energy consumption rates in nodes. The adaptive clustering is required in Industrial Internet of Things (IIoT) based sophisticated networks where seamless connectivity is imperative for rapid communication. In order to meet this research gap, an adaptive clustering algorithm for mobile opportunistic networks is proposed within military bases that uses a heuristic algorithm (GA) for adaptive clustering. An analysis of the opportunity network at the connected military base is carried out and the mobile opportunity model is constructed using the adaptive clustering for the similar traffic. In a mobile machine network, the next hop node is determined by the node clustering principle. A LEACH clustering protocol enables communication between cluster heads and base stations based on single-hop and multihop cluster nodes. In order to perform adaptive clustering of mobile network nodes based on network partitioning and scheduling of clusters, genetic algorithms are used. The proposed approach can be applied to the IIoT systems in places where adaptive clustering is required to optimize energy consumption, to reduce latency rates, and to enhance the throughput of mobile networks. The experimental findings suggest that the proposed adaptive strategy is capable of optimizing energy consumption rates, reducing network latency, and boosting efficiency by increasing throughput in mobile opportunistic networks.