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.
Published Version
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
