LBR-GWO: Layered based routing approach using grey wolf optimization algorithm in wireless sensor networks

The sensors deployed inside a wireless sensor network (WSN) have limited energy sources, significantly impacting the network throughput. This article's research objective is to increase network lifetime by developing an energy-efficient layered-based routing algorithm for WSNs using the grey wolf optimization (LBR-GWO) algorithm. In this, the grey wolf's leadership hierarchy has followed, which improves the network's energy capability. The entire region of the deployed nodes divides into four layers. In these nodes, layer one is chosen as cluster heads. If more than two nodes are present in layer one, then the cluster head is selected based on the game theory model; otherwise, the decision is made based on the node's residual energy. While the existing algorithm has several complex control parameter points, the current algorithm has fewer complex parameters. Therefore, in comparison to other algorithms, this algorithm is easy to apply in cluster-based sensor networks. Simulation findings prove the LBR-GWO algorithm supremacy for balancing energy consumption across the nodes and improving the network's lifetime compared to the LEACH, HEED, and PSO protocols.