An energy-aware clustering method in the IoT using a swarm-based algorithm

Internet of Things (IoT) is a set of interrelated devices on the Internet platform. It can receive and send data to make human life more efficient and convenient. The main challenge in the IoT network is energy consumption in nodes. Clustering is a proper data collection method in the IoT that selectively reduces energy consumption by forming IoT nodes into clusters. The Cluster Head (CH) can control all Cluster Member (CM) nodes, and all intra-cluster and inter-cluster connections are made through it. Today, metaheuristic algorithms solve many problems, including clustering, because they have good performance and are noticeable practical effects. This paper uses the artificial fish swarm algorithm, an effective algorithm to solve optimization problems based on imitation of fish behavior. The cost function contains the residual energy of the nodes, the sum of the distances, and the degree of each node. The simulation results on the dataset showed that the proposed method increases network lifetime value by at least 12.5% and reduces latency by at least 14%.