Network Coding Enhanced Forwarding Strategy with Predictive Content Discovery in V-NDN

Vehicular named data networking (V-NDN) is promising to match dynamic topology of vehicular ad hoc networks. However, it has some potential issues such as broadcast storm of Interest packets due to flooding and blind forwarding strategy at intermediate nodes. To address these issues, we propose a network coding enhanced forwarding strategy with predictive content discovery (NC-PCD) mechanism which functions at key nodes (e.g., road intersections). First, content providers provide innovative encoded data packets. Second, the NDN packet formats are extended to carry the information about the innovative degree, distance, congestion level and path quality, etc. Finally, the NDN router at the key node collects information to select the optimal forwarding direction for subsequent requests. This problem is formulated as a joint optimization problem and solved by deep reinforcement learning. Simulation results show that the proposed NC-PCD is significantly superior to the existing forwarding strategies in terms of Interest satisfied ratio, response delay and number of transmitted packets.