A novel approach for clustering and routing in WSN using genetic algorithm and equilibrium optimizer. (18th March 2022) - Heidari, Ehsan; Movaghar, Ali; Motameni, Homayun; Barzegar, Behnam
Summary: The Internet of Things (IoT) is a new concept in the world of technology and information and has many applications in industry, communications, and various other fields. In the lowest layer of the IoT, wireless sensor networks (WSNs) play an important and pivotal role. WSN consists of a large number of sensors and is commonly used to monitor a target. It is important to reduce energy consumption in WSNs to extend network life, since it is usually impossible to replace sensor batteries. In this paper, a novel clustering and routing method is proposed. It is mainly based on genetic algorithms and equilibrium optimization. To improve scalability, the sensor nodes are clustered in the first phase using the genetic algorithm and then the best cluster heads are selected. It follows by the next step, where each node sends its collected data to the cluster head. The cluster heads first aggregate the received data and then use a multihop optimization routing algorithm by the new equilibrium optimization algorithm to send the data to the base station. Specifically, the main contribution of this work is to reduce energy consumption in WSN using a genetic algorithm for clustering and an equilibrium optimization algorithm for choosing the path between the cluster heads and the base station. The proposed method was simulated in MATLAB software, and it was compared with five other methods. It was observed that the proposed method outperforms other methods in terms of energy consumption, network lifespan, and the number of packages delivered. Abstract : In this paper, to increase the lifespan of the network, a new clustering and routing method is presented. First, a genetic algorithm is used for clustering and selecting the optimal cluster heads, and then a new Equilibrium Optimizer (EO) metaheuristic algorithm is applied to send data from the cluster heads to the base station (BS). The results of network performance analysis show that the proposed method can improve network performance in terms of energy consumption, network life, and number of packets delivered.