The Hybrid Neuro-Genetic Optimization Framework (HNGOF) for Enhanced Performance in IoT Smart Agriculture

The rapid expansion of the Internet of Things (IoT) has revolutionized the agricultural sector, giving rise to Smart Agriculture systems that promise enhanced efficiency, resource management, and yield prediction. However, the performance of these systems, particularly those relying on Wireless Sensor Networks (WSNs) operating in Low-Power and Lossy Networks (LLNs), is often hampered by inherent limitations in routing efficiency, high communication latency, and energy consumption. This paper introduces the Hybrid Neuro-Genetic Optimization Framework (HNGOF), a novel metaheuristic approach designed to intelligently optimize routing decisions in IoT-enabled smart agricultural environments. HNGOF synergistically combines the adaptive learning capabilities of Artificial Neural Networks (ANN) with the robust global search efficiency of Genetic Algorithms (GA). By dynamically learning network conditions and optimizing the routing topology based on predefined fitness functions related to residual energy and network stability, HNGOF demonstrably outperforms existing routing protocols. Experimental validation across simulated smart farm scenarios indicates that HNGOF achieves a significant reduction in average end-to-end latency by approximately ۲۵% and improves overall network throughput by ۳۲% compared to conventional methods, ensuring more reliable Energy Efficiency.