Balancing Energy Consumption and Network Longevity: A Review of LEACH Protocol Enhancements through Computational Intelligence

Abstract

Low Energy Adaptive Clustering Hierarchy (LEACH) is the most widely used protocol for clustering in wireless sensor networks (WSNs). These networks are composed of numerous Sensor Nodes (SNs) that are employed for data monitoring and collection from the environment. We utilized the Systematic Literature Review (SLR) approach to study a total of 960 papers from 2020 to 2025 using IEEE Xplore, Web of Science, Springer Link, Science Direct, and Scopus databases. Google Scholar was used as an additional database for verification purposes only. Research gaps and potential directions for hybrid model development were identified based on Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. The study focused on LEACH protocol enhancements through computational intelligence associated with WSNs. Reviews indicate that 22% of the reviewed papers used artificial intelligence (AI) approach, 48% of the reviewed papers used fuzzy approach, and 30% of the reviewed papers used genetic approach based on adaptability and scalability. Each of the proposed approaches consistently outperforms traditional LEACH in energy balance but couldn’t balance energy consumption and network longevity with optimal performance. The findings demonstrate the persistent challenges in communication overhead, scalability, and adaptability. The paper argued that hybrid computational intelligence frameworks offer the most promising direction for enhancing LEACH-based WSNs toward sustainable, energy-efficient, and scalable performance.