Batool Faryal, Ali Kamran, Lasebae Aboubaker, Windridge David, Kiyani Anum
Department of Computer Science, Middlesex University, London NW4 4BT, UK.
Sensors (Basel). 2025 Aug 23;25(17):5250. doi: 10.3390/s25175250.
Wireless Sensor Networks (WSNs) are very important for monitoring complex 3D environments like forests, where energy efficiency and reliable communication are critical. This paper presents EEL-GA, an Energy Efficient LEACH-based clustering protocol optimized using a Genetic Algorithm. Cluster head (CH) selection is guided by a dual-metric fitness function combining residual energy and intra-cluster distance. EEL-GA is evaluated against EEL variants using Particle Swarm Optimization (PSO), Differential Evolution (DE), and the Artificial Bee Colony (ABC) across key performance metrics, including energy efficiency, packet delivery, and cluster lifetime. Simulations using real environmental data confirm EEL-GA's superiority in sustaining energy, minimizing delay, and improving network stability, making it ideal for smart forestry and mission-critical WSN deployments. The model also incorporates environmental dynamics, such as temperature and humidity, enhancing its robustness in real-world applications. These findings support EEL-GA as a scalable, adaptive solution for future energy-aware 3D WSN frameworks.
无线传感器网络（WSN）对于监测诸如森林等复杂的三维环境非常重要，在这些环境中，能源效率和可靠通信至关重要。本文提出了EEL-GA，一种基于遗传算法优化的节能型基于LEACH的聚类协议。簇头（CH）选择由结合剩余能量和簇内距离的双指标适应度函数引导。使用粒子群优化（PSO）、差分进化（DE）和人工蜂群（ABC）算法，针对包括能源效率、数据包传递和簇生命周期在内的关键性能指标，对EEL-GA与EEL变体进行了评估。使用真实环境数据进行的模拟证实了EEL-GA在维持能量、最小化延迟和提高网络稳定性方面的优越性，使其成为智能林业和关键任务WSN部署的理想选择。该模型还纳入了温度和湿度等环境动态因素，增强了其在实际应用中的鲁棒性。这些发现支持EEL-GA作为未来能源感知型三维WSN框架的可扩展、自适应解决方案。
