Hybrid RBFN-GWO Approach for Energy-Efficient and Secure Routing in Intelligent IoT Networks for Precision Farming

Abstract

This paper introduces a hybrid approach combining Radial Basis Function Networks (RBFN) and Grey Wolf Optimization (GWO) to address the challenges of energy efficiency and security in Intelligent IoT networks for precision farming. Our proposed method utilizes RBFN for feature extraction and classification of network states, while GWO optimizes the routing parameters to achieve an optimal balance between energy conservation and security. The hybrid RBFN-GWO algorithm adapts to dynamic network conditions and evolving security threats in real-time. Extensive simulations using real-world precision farming data demonstrate that our approach outperforms existing protocols, achieving a 20% increase in network lifetime and a 15% improvement in intrusion detection accuracy. This research contributes to the development of more efficient and secure IoT infrastructures for precision agriculture applications.