Improved Tuna Optimization-Based Energy Efficient Clustering Protocol for Internet of Things

In smart applications, Internet of Things (IoT) serves as a backbone to facilitate a minimized cost monitoring system due to recent advancements in the technology of sensing and networking. Smart sensors are utilized in diversified number of real-time applications over the recent years to control and monitor physical objects deployed over the implementation environment. This adoption of smart sensors over the real-time applications generates a huge amount of data in the form of text, number, image, and video, which in turn incurs massive amount of energy during the data transmission process. In this context, clustering is ideal for achieving data collection and exchange over the network, and majority of the researchers utilized optimization algorithms for selecting optimized cluster head (CHs) in the network. In this paper, an Improved Tuna Swarm Optimization Algorithm (HSITSOA)-based Clustering Scheme is proposed for CH rotation in the cluster with the objective to reduce algorithmic convergence time which is considered as one of the predominant limitations of the existing clustering approaches. This proposed HSITSOA approach comprises of the processes of cluster construction and CH selection which is established through the balanced trade-off attained during the exploration and exploitation. The simulation conducted using MATLAB R2019a confirmed its efficacy in determining the best CHs on the cluster. It was also identified to improve the packet delivery rate and network lifetime by 23.49% and 27.98%, better than the benchmarked approaches