Energy efficiency in wireless sensor networks: comparing traditional and advanced clustering protocols

Abstract Wireless networks have experienced tremendous growth, particularly in the telecommunications sector, with various technologies such as cellular networks, Wireless Local Area Networks (WLAN), Bluetooth, Mobile Ad-hoc NETworks (MANETs), Ultra-WideBand (UWB), and Wi-Max. Multi-hop wireless networks, composed of diverse devices like wireless transceivers, provide advanced storage, forwarding capabilities, and efficient media access techniques. Among these, Wireless Sensor Networks (WSNs) have gained significant attention due to their versatility and applications in military, environmental monitoring, and smart infrastructure. The increasing use of WSNs in energy-constrained environments has necessitated the development of energy-efficient protocols to extend network lifetime and improve overall performance. This study conducts a comprehensive simulation-based evaluation of various energy-efficient clustering protocols for WSNs, focusing on Low-Energy Adaptive Clustering Hierarchy (LEACH), Improved-LEACH (I-LEACH), Hybrid Energy-Efficient Distributed Clustering (HEED), Power-Efficient GAthering in Sensor Information Systems (PEGASIS), Energy-Efficient Cluster Head (EECH), Flower Pollination Algorithm (FPA), and Termite Colony Optimization (TCO). Key performance metrics such as Total Energy Consumption (TEC), Packet Delivery Ratio (PDR), Throughput, Packet Loss, Cumulative Energy Consumption (CEC), and Residual Energy (RE) were analyzed to identify protocol-specific strengths and weaknesses. The simulation results reveal significant differences in protocol performance, with FPA demonstrating superior energy efficiency and network reliability, achieving improvements of up to 30% in TEC, 35% in throughput, 7% in PDR, 12% in packet loss, 20% in CEC, and 25% in RE compared to other protocols. These findings provide valuable insights into the trade-offs associated with clustering protocols, aiding in the selection of the most suitable methodology for specific WSN applications. The research work advances the understanding of clustering protocols by presenting a data-driven comparative analysis that evaluates their fitness for energy-constrained scenarios. The study contributes to optimizing protocol selection for enhanced network lifetime and performance, paving the way for more efficient WSN deployments.