An Improved Sunflower Optimization Algorithm for Cluster Head Selection in the Internet of Things

Abstract

Due to the widespread of smart devices and services, the Internet of Things (IoT) has gained attention from researchers and is still in constant development. Many challenges face the IoT networks and need to be solved. Reducing energy consumption to increase the network lifetime is the main issue among these challenges. The clustering approach is one of the best solutions to solve this issue. Choosing the best Cluster Heads (CHs) can consume less energy in the IoT-WSN. Swarm Intelligence (SI) algorithms can help to solve complicated problems. In this paper, we propose a novel algorithm to select the best CHs in the IoT-WSN. The novel algorithm is called an Improved Sunflower Optimization Algorithm (ISFO). In the ISFO, we combine the Sunflower Optimization Algorithm (SFO) with the lèvy flight operator. Such invoking can balance the diversification and intensification processes of the proposed algorithm and avoid trapping in local minima. We compare the ISFO algorithm with six SI algorithms. The results of the proposed algorithm show that it can consume less energy than the other algorithms, also the number of nodes still alive for it is larger than alive nodes for the other algorithms. Hence, the ISFO algorithm proved its superiority in reducing the consumed energy and increasing the lifetime of the network.