Energy based multi objective golden jackal optimization for cluster based routing in wireless sensor network

Wireless Sensor Network (WSN) is a promising domain that is gaining more attention because of its applicability and suitability in modern applications that comprise health care, disaster management, and environment monitoring purposes. Energy efficiency is considered an important issue because of the restricted energy of non-rechargeable batteries in the sensor. Cluster based routing is significant in handling the issue of energy efficiency in the WSN. In this paper, the Energy-based Multi Objective Golden Jackal Optimization (EMOGJO) is proposed for enhancing energy efficiency. The EMOGJO is used to select the optimum Cluster Heads (CHs) by using the Mean Node Energy (MNE), Individual Node Neighborhood Count (INNC), the interspace between sensors, the interspace between CH and BS, and node degree. Subsequently, the route through CHs until Base Station (BS) is discovered using the EMOGJO which is optimized by energy, and interspace between CH and BS measures. Therefore, the EMOGJO is used to enhance life expectancy while increasing the throughput. The EMOGJO method is evaluated using alive nodes, energy consumption, data packets received in BS, throughput, packet loss ratio and life expectancy. The EMOGJO is compared with the existing approaches that are, Hybrid Improved Whale Artificial Ecosystem Optimization (HIWAEO), Butterfly Optimization Algorithm (BOA)-Ant Colony Optimization (ACO), and Energy Efficient Cluster Based Routing Protocol using Firefly Algorithm namely EECRAIFA. The life expectancy of EMOGJO for 350 nodes is 17,778 rounds, which is greater than the HIWAEO’s 1590 rounds, for the same number of nodes.