MOTCO: Multi-objective Taylor Crow Optimization Algorithm for Cluster Head Selection in Energy Aware Wireless Sensor Network

Wireless Sensor Networks (WSNs) are widely employed for sensing and collecting the data of an environment from a remote area and are used for a variety of engineering applications. The data transfer between the nodes is enabled using the routing protocols that face huge challenge in terms of energy. With energy as an effective constraint, the energy-aware routing is proposed using the optimal cluster head selection procedure. The proposed method of selecting the optimal cluster head is performed using Multi-Objective Taylor Crow Optimization (MOTCO) algorithm that is the combination of the Taylor series and the Crow Search Algorithm (CSA). The proposed objective function is based on the distance between the nodes in the cluster, energy of the nodes, traffic density of the cluster, and the delay in transmitting the data packets. The designed objective function is tuned for a minimum value and the cluster head corresponding to the minimum value of the objective function becomes the optimal cluster head. The simulation is carried out by considering 50 nodes and 100 nodes in the WSN environment for analysis. The analysis proves that the proposed MOTCO outperforms the existing methods by attaining the network energy and throughput at a maximum value of 10% and 65% at the 2000th round.