Energy Efficient Cluster Based Routing Using Multiobjective Improved Golden Jackal Optimization Algorithm in Wireless Sensor Networks

In recent decades, the Wireless Sensor Networks (WSNs) have played a prominent role in different fields because of cost efficiency and energy efficiency. However, sensor nodes deployed in WSNs are generated by batteries which may drain all their energy after a certain period. The process of clustering assists in enhancing network lifespan thereby minimizing an energy consumption. A lifetime expectancy of WSNs can be improvised by selecting the optimal Cluster Head (CH) and optimal shortest path to transmit data packets. A maintenance of energy efficiency in WSN is a challenging process due to constrained sources that cannot be operated for a longer time. So, this research focuses on energy efficiency and introduces the Multiobjective Improved Golden Jackal Optimization Algorithm (MIGJOA). The MIGJOA helps to choose CHs and optimal routing path to transmit data. A fitness objectives like the distance among neighbor node and Base Station (BS), distance between BS and CH, node degree, and mean node energy are employed as fitness functions to select optimal CHs. The efficiency of the suggested technique is assessed with Adaptive Blackhole Tuna Swarm Optimization (ABTSO), Hybrid African Vultures Cuckoo Search Optimization (HAVCSO), Butterfly Optimization Algorithm-Ant Colony Optimization (BOA-ACO) based on alive nodes, normalized energy and consumption of average energy. The alive nodes of proposed approach when a number of rounds is 2500 is 97 whereas the alive node count in the existing BOA-ACO is 89.