Energy-Efficient Routing Protocols for Wireless Sensor Networks (WSNs)
DOI:
https://doi.org/10.15662/IJARCST.2020.0302001Keywords:
Wireless Sensor Networks (WSNs), Energy-Efficient Routing, LEACH, PEGASIS, Directed Diffusion, Geographic Routing, Network Lifetime, Data AggregationAbstract
Energy-efficiency is a fundamental challenge in Wireless Sensor Networks (WSNs), where small sensor nodes operate on limited battery resources and are often deployed in inaccessible environments. This paper investigates energy-efficient routing protocols designed to extend network lifetime, maintain connectivity, and optimize data delivery. Focusing on pre-2019 developments, we review and analyze prominent routing schemes, including data-centric, hierarchical (cluster-based), geographic, and multipath techniques. Our study introduces a methodology that classifies protocols based on network architecture, energy-aware metrics, and routing mechanisms. We present evaluation metrics such as energy consumption per packet, network lifetime (measured by first node death and last node death), data delivery ratio, and latency. The workflow entails protocol selection, simulation in environments modeled after LEACH, PEGASIS, Directed Diffusion, and geographic routing frameworks, with parameter variation and comparative analysis. Key findings show that hierarchical protocols like LEACH and PEGASIS significantly reduce per-node energy usage through data aggregation, whereas geographic and multipath protocols optimize route selection to balance load and improve resilience. However, trade-offs arise: cluster formation overheads, synchronization complexities, uneven cluster head rotation, and suboptimal performance in dynamic topologies. Our results and discussion reflect how these protocols perform under various node densities and traffic scenarios, revealing context-dependent advantages. We conclude that no single protocol universally outperforms across all metrics, but hybrid approaches and adaptive clustering mechanisms yield promising energy gains. For future work, we propose integrating machine-learning-based cluster head selection, mobility awareness, and energy harvesting models to further improve network sustainability. This analysis offers a comprehensive snapshot of pre-2019 energy-efficient routing strategies in WSNs and lays the groundwork for next-generation protocols that can dynamically adapt to network conditions while maximizing energy efficiency.
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