An energy optimized approach for the design of wireless sensor networks

Abstract

In today's data-driven world, we need to ensure reliable wireless access as it is vital. The increasing demand for data connectivity has significantly raised power consumption in wireless networks. Wireless Sensor Networks (WSNs), which rely on battery power, face significant challenges due to this vulnerability. WSNs are widely utilized in various applications, including environmental monitoring, healthcare, industrial machinery, and security. Extending their operational lifetime is crucial for ensuring sustainable network performance. The primary limitation of WSNs lies in their energy-constrained nature. As nodes run out of energy, they begin to fail, resulting in reduced network coverage, connectivity, and throughput. While numerous protocols and algorithms have been proposed to extend network lifetime, overcoming power limitations remains a significant challenge. Existing heuristic approaches provide near-optimal solutions but are often impractical for real-time implementation. The goal of this research is to develop and assess energy-efficient algorithms that minimize power consumption and prolong the lifespan of WSNs. The specific objectives are to design an algorithm that optimizes energy usage through efficient routing and clustering, to balance energy consumption among nodes, reducing the likelihood of premature node failures, and to improve network throughput. This study adopts a simulation-based approach to evaluate the proposed algorithm. An energy-aware protocol is implemented to evenly distribute network workload and minimize energy wastage. The research contributes by demonstrating that heuristic solutions can be enhanced to achieve near-optimal performance, closing the gap between offline optimization and practical, real-time deployment. Simulation results indicate that the proposed algorithm improves network lifetime and energy efficiency. Specifically, it achieves over 50% improvement in First Node Death (FND), 14% improvement in Last Node Death (LND), and 37% overall energy savings compared to conventional approaches. These results confirm that better energy balancing significantly enhances WSN sustainability and supports their long-term use across diverse applications.