V2V Composite Routing Algorithm for Internet of Vehicles Based on Improved Q-Learning

Targeting the high maintenance costs and poor dynamic adaptability of existing routing algorithms for the Internet of vehicles, this study introduces a multi-hop clustering composite routing algorithm that builds upon an enhanced Q-learning approach. In the cluster maintenance phase, the cluster head selects the node with good communication performance and relatively stable speed with the edge vehicles as the gateway node according to the utility function of the gateway. During the route establishment stage, the link communication quality during the route establishment stage, the link communication quality and packet transmission direction are taken into account, and node mobility, a node performance evaluation function evaluates the comprehensive performance of the selected next-hop node to avoid the blind path problem. In the Q-learning stage, the duration and distance of adjacent links are quantified and employed as learning and discount rates to enhance the efficiency of Q-learning. Simulations on mobile datasets on Cologne, Germany and a city in China show that, compared with the Random, RSAR routing algorithms, the routing lifetime and throughput of the proposed algorithm improved by 16.57% and 31.54% on average respectively, and the communication delay and packet loss rate decreased by 13.5% and 64.32% on average respectively. These results indicate that the proposed algorithm can adapt to complex and Vehicular Ad Hoc Networks (VANETs).