Enabling Rural IoV Communication through Decentralized Clustering and Federated Learning

The Internet of Vehicles (IoV), a network interconnecting vehicles through sensors and communication technology, holds promise for addressing traffic congestion and enhancing road safety through vehicle-to-vehicle and vehicle- to-infrastructure communication. While the potential benefits are evident, the integration of IoV in rural environments faces challenges due to inadequate infrastructure, rendering current centralized solutions impractical. This challenge is amplified by latency considerations, especially in applications critical for safety. In response, we present a decentralized clustering algorithm specifically designed for the implementation of IoV in rural areas, with enhancements from federated deep reinforcement learning. Our approach not only demonstrates low network latency, competitive packet delivery ratios, and cluster stability but also transcends the limitations of conventional solutions. We present a viable solution for IoV in rural settings, our research contributes to the realization of safer and more efficient transportation systems. The proposed approach not only addresses current challenges but also lays the groundwork for future applications in varied scenarios.