Adaptive Grasshopper Optimization Algorithm based Dynamic Cluster Head Selection and Energy Efficient Routing for Wireless Sensor Network (WSN)

Wireless Sensor Network (WSN) clustering is useful for developing routing algorithms that improves the scalability and longevity of the network. Cluster Head (CH) is essential for data transmission in the clustered WSN paradigm. Thus several cluster-based routing protocols have been introduced recently. As such, energy constraints affect both the duration of the network and energy-efficient routing. In this paper, Adaptive Grasshopper Optimization Algorithm (AGOA) is introduced for cluster-based routing and CH selection which increases routing efficiency, and maximizes network lifetime. Furthermore, high convergence rate and lower parameter complexity as compared to other optimization techniques. Here, Residual Energy (RE), distance to the Base Station (BS), distance to neighbors, node degree, and node centrality has been used for choosing the best CH. Additionally, the path between the BS and CH can be determined using AGOA. Lastly, the results routing protocols are evaluated using metrics including Packet Delivery Ratio (PDR), Packet Loss Ratio (PLR), Average energy consumption, and Network Lifetime.