Vijayalakshmi K, Maheshwari A, Saravanan K, Vidyasagar S, Kalyanasundaram V, Sattianadan D, Bereznychenko Victoriia, Narayanamoorthi R
Department of Computational Intelligence, School of Computing, College of Engineering and Technology, SRM Institute of Science and Technology, Kattankulathur, Chennai, 603203, India.
Department of Electrical and Electronics Engineering, SRM Institute of Science and Technology, Kattankulathur, 603203, Tamil Nadu, India.
Sci Rep. 2025 Mar 27;15(1):10644. doi: 10.1038/s41598-025-94751-2.
Recent advances in wireless communication have enabled the development of small, low-cost, wearable sensors, which play a crucial role in applications such as healthcare monitoring, environmental sensing, and industrial automation. However, maximizing network lifetime (NL) and optimizing energy consumption remain key challenges in Wireless Sensor Networks (WSNs). Existing routing algorithms often struggle to balance energy efficiency and service quality, leading to premature network failures. To address this gap, this paper proposes a novel approach that integrates a near-optimal Single Objective Genetic Algorithm (SOGA) and an Advanced Exhaustive Search Algorithm (AESA) to enhance NL in fully connected WSNs. The proposed method optimizes energy-efficient routing by incorporating four quality parameters: proximity ranging, network lifetime, interaction counting, and link effectiveness. By leveraging multi-hop communication and efficient node connectivity, our approach significantly outperforms existing routing protocols in terms of energy conservation and data transmission reliability. A network simulator is utilized to evaluate key performance indicators, including average edge delay, latency, and packet delivery ratio, comparing them against conventional routing protocols such as ad hoc on-demand routing discovery, dynamic source routing, and simplified connection state routing. The findings demonstrate that the proposed method effectively extends NL while maintaining an optimal balance between energy consumption and service quality. This research contributes to the state of the art by providing an advanced energy-efficient routing solution for WSNs, with potential implications for various real-world applications, including smart cities, healthcare monitoring, and industrial IoT deployments.
无线通信领域的最新进展推动了小型、低成本可穿戴传感器的发展，这些传感器在医疗保健监测、环境传感和工业自动化等应用中发挥着关键作用。然而，最大化网络寿命（NL）和优化能源消耗仍然是无线传感器网络（WSN）中的关键挑战。现有的路由算法往往难以平衡能源效率和服务质量，导致网络过早失效。为了弥补这一差距，本文提出了一种新颖的方法，该方法集成了近最优单目标遗传算法（SOGA）和高级穷举搜索算法（AESA），以提高全连接WSN中的NL。所提出的方法通过纳入四个质量参数来优化节能路由：近距离测距、网络寿命、交互计数和链路有效性。通过利用多跳通信和高效的节点连接性，我们的方法在节能和数据传输可靠性方面显著优于现有的路由协议。利用网络模拟器评估关键性能指标，包括平均边缘延迟、延迟和数据包交付率，并将它们与传统路由协议（如按需自组织路由发现、动态源路由和简化连接状态路由）进行比较。研究结果表明，所提出的方法有效地延长了NL，同时在能源消耗和服务质量之间保持了最佳平衡。这项研究通过为WSN提供先进的节能路由解决方案，为该领域的发展做出了贡献，对包括智慧城市、医疗保健监测和工业物联网部署在内的各种实际应用具有潜在影响。
