September 12, 2025

Hybrid Gannet Optimization Algorithm-based Energy Efficient Cluster Routing Mechanism for extending Network Longevity in Sensor Networks

Key Points

The proposed hybrid gannet optimization algorithm improved network lifespan by 23.48%, enhancing energy efficiency.
By minimizing energy consumption by 25.84%, the study shows significant gains in the operational efficiency of sensor nodes.
The clustering technique utilized addresses challenges linked to reduced energy consumption and node density in wireless sensor networks.
Utilizing a genetic algorithm for route selection among clusters, the method maximized packet delivery ratio by 25.68%.

Abstract

Abstract In the context of smart computing, development of Wireless Sensor Networks (WSNs) is highly notable for its indispensable role in monitoring and facilitating reactive decision making in different areas of applications. These WSNs comprise of small and self-configured Sensor Nodes (SNs) with battery power, which introduces energy and resource-constrained communication during their deployment. Increased energy consumptions of these deployed SNs adversely affect network lifetime and energy stability. Thus, design and implementation of energy efficient routing protocol for WSNs that helps in attaining both energy stability and network lifetime of SNs is a herculean task. The implementation of clustering techniques helps in determining optimal solutions that helps in addressing the challenges associated with reduced energy consumptions of SNs. Most of the existing clustering solutions of WSNs parameters including scalability, energy balancing and node density during the process of choosing higher energy SNs as Cluster Heads (CHs) in the network. In this paper, a Hybrid Gannet Optimization Algorithm-based Energy Efficient Cluster Routing (HGOAECR) mechanism is proposed for selecting optimal number of clusters that attribute towards better energy stability and prolonged network lifetime. It used Gannet Optimization Algorithm (GOA) and Enhanced Whale Optimization Algorithm (EWOA) for attaining the optimal selection of CHs through the evaluation of factors that includes node centrality, node degree, distance to the Base Station (BS), distance to neighbour and Residual Energy (RE) of SNs. It specifically used Genetic Algorithm (GA) for determining the path amid chosen CHs and sink by selecting potential route with respect to node degree, RE and distance. The performance evaluation of this proposed HGOAECR confirmed improved network lifespan of 23.48%, minimized energy consumption of 25.84% and maximized Packet Delivery Ratio (PDR) to the sink nodes by 25.68% when compared to benchmarked schemes taken for comparison.

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