Intelligent route optimization for internet of vehicles using federated learning: promoting green and sustainable IoT networks

Abstract

As the internet of vehicles (IoV) continues to evolve, optimizing vehicle routing becomes increasingly important for enhancing traffic efficiency and minimizing environmental impact. This paper introduces an intelligent vehicle route optimization protocol leveraging federated learning (FL) to achieve green and sustainable IoV systems. By distributing the learning process across multiple edge devices, the proposed protocol minimizes the need for centralized data processing, reducing network congestion, and preserving user privacy. The system optimizes vehicle routes based on real time traffic conditions, fuel efficiency, and carbon emissions, and promoting greener transportation practices. Simulations conducted in a dynamic IoV environment demonstrate significant improvements in route efficiency, fuel consumption, and carbon emissions. The results underscore the potential of FL in transforming IoV routing by balancing performance and sustainability, making it a promising solution for the future of connected transportation.