FOAEAUC-SARP: A novel energy-efficient protocol integrating unequal clustering and intelligent routing for sustainable wireless sensor networks

Abstract

Unequal clustering (UC) integrated with intelligent routing approaches offers a significant advancement in optimizing energy consumption and extending the lifetime of Wireless Sensor Networks (WSNs). Unlike traditional clustering methods that uniformly allocate energy-intensive tasks among sensor nodes (SNs), UC leverages the heterogeneity in node capabilities, assigning high-energy nodes as cluster heads (CHs) for data aggregation and transmission, while low-energy nodes perform lighter tasks. This hierarchical structure enhances energy efficiency and prolongs network longevity. While existing research has explored optimization techniques for clustering and routing, this study introduces the novel integration of the Fox Optimization Algorithm (FOA) and Snake Algorithm (SA) in the FOAEAUC-SARP framework to address energy balancing and routing challenges in heterogeneous WSNs. FOAEAUC-SARP employs a two-stage process: FOA-based unequal clustering for CH selection and SA-based routing for optimal data transmission. Through extensive simulations analysis, the proposed FOAEAUC-SARP demonstrates superior energy efficiency and performance compared to state-of-the-art models, addressing key limitations of prior approaches and offering a robust solution for advancing WSN performance. The analysis was conducted for three different case scenarios which are replicating with the real-world applications and comparing the performance of the proposed approach various existing approaches. The measured results show that there is a significant difference with the other methods in all the performance measures.