Energy-efficient cluster head selection in wireless sensor networks-based internet of things (IoT) using fuzzy-based Harris hawks optimization

The Internet of things (IoT) has become a cornerstone of the fourth industrial revolution. IoT sensor devices in the network are provisioned with limited resources, such as little processing speed, minimal computing capacity, and less power. Furthermore, IoT devices are battery-powered, which cannot provide battery sufficiently to some applications resulting in an energy scarcity problem. Clustering is an efficient method in IoT networks to save energy. Nodes can coordinate communication by selecting an optimal cluster head (CH) within the cluster and transmitting information to a central node or sink. The CH minimizes energy consumption associated with communication overhead and extends the overall lifespan of the network by facilitating coordination between clusters and the central server. Many existing optimization techniques have proposed CH selection to improve the network's lifespan but all the existing algorithms on CH selection are not practical due to the long convergence time. This research paper proposes a novel fuzzy-based Harris Hawks Optimization (FHHO) algorithm that chooses optimal CH considering Residual energy (RER) and distance between sink and node. The fitness function is evaluated using fuzzy logic over maximization and minimization network parameters. Extensive experimentations were conducted to test and validate the performance of proposed FHHO algorithm on MATLAB 2019a tool. And, the results stated that the proposed method FHHO has better results as compared to other CH selection techniques, namely, PSO-ECHS, FIGWO, and GWO-C, in network lifespan by 18–44% and throughput by 5–20%.