Energy efficient cluster based routing for wireless sensor networks using moth levy adopted artificial electric field algorithm and customized grey wolf optimization algorithm

Clustering is an effective strategy for creating routing algorithms in Wireless Sensor Networks (WSNs), which increases the network's lifetime and scalability. In the clustered WSN, the Cluster Head (CH) plays a vital role in data transmission. So far, much research work has already existed in regards to cluster-based routing. Despite this, they have challenges with fault tolerance, unequal load balancing, and local optimal solutions. To address these problems, this research presents a novel method for cluster based routing that makes the routing progress more effective to maximize the network lifetime. This has been carried out under two phases: selecting the optimal cluster head via the new Moth Levy adopted Artificial Electric Field Algorithm (ML-AEFA), and the data transmission is carried out by the new Customized Grey Wolf Optimization (CGWO) algorithm. Here, the selection of the optimal CH is performed under the consideration of energy, node degree, distance among the sensor nodes, distance among the CH and Base Station (BS), and time of death node. Finally, the implemented method's performance is compared to that of existing schemes using various measures. In particular, the network life time of the proposed work for scenario 1(number of nodes = 100) is 35.77%, 35.77%, 35.04%, 34.43%, and 33.08% better than the existing GWO, MSA, AEFA, BOA + ACO, and improved ACO methods respectively.