A Road-aware Approach for Hierarchical Routing in IoV based on Intents and Q-values

Nowadays, intelligence is paving its ways into the IoV domain. With the need of improvement in intelligence in this area, the need of self-organizing network management systems for providing V2X communication is also of vital importance, which current systems lack. Current routing solutions for IoV are complex and require intelligence embedded in the form of closed-loop systems. To this, an intent-based system is designed, which takes high level requirements for routing in IoV.The routing approach is road-aware and widens the scope of road-awareness to vehicles from multiple edge domains. Another problem with the current routing schemes in IoV is that their network management systems are not self-organizing. A self-organizing management system is of high importance and requires a closed-loop system. To this, an intent-based approach is followed integrated with a reinforcement learning model that supports the creation of a routing policy, which is further applied to the orchestrator and enables a road-aware and hierarchical routing approach. The proposed system is shown to be efficient in terms of data rate and the number of packet flows managed per unit time, in the management of vehicular networks.