Lack of a fully vehicular topology view and restricted vehicles' movement to streets with the time-varying traffic light conditions have caused drastic gaps in the traditional vehicular routing protocols. A hierarchical traffic light-aware routing scheme called HIFS is proposed in this paper using fuzzy reinforcement learning and software-defined network (SDN) to fill these gaps. At the first level of our HIFS scheme, a utility-based intersections selection policy is presented using fuzzy logic that jointly considers delay estimation, curve distance, and predicted of moving vehicles towards intersections. Then, a fuzzy logic-based path selection policy is proposed to choose the paths with highest flexibility against the intermittent connectivity and increased traffic loads. Residual bandwidth, Euclidean distance, angular orientation, and congestion are considered inputs of the fuzzy logic system. Meanwhile, traffic light states and nodes' information are used to tune the output fuzzy membership functions via reinforcement learning algorithm. The efficiency of our scheme in controlling ambiguity and uncertainty of the vehicular environment is confirmed through simulations in various vehicle densities and different traffic lights duration. Simulation results show our HIFS scheme's superiority over the state-of-the-art methods in terms of delivery ratio, average delay, path length, and routing overhead.

Lack of a fully vehicular topology view and restricted vehicles' movement to streets with the time-varying traffic light conditions have caused drastic gaps in the traditional vehicular routing protocols. A hierarchical traffic light-aware routing scheme called HIFS is proposed in this paper using fuzzy reinforcement learning and software-defined network (SDN) to fill these gaps. At the first level of our HIFS scheme, a utility-based intersections selection policy is presented using fuzzy logic that jointly considers delay estimation, curve distance, and predicted of moving vehicles towards intersections. Then, a fuzzy logic-based path selection policy is proposed to choose the paths with highest flexibility against the intermittent connectivity and increased traffic loads. Residual bandwidth, Euclidean distance, angular orientation, and congestion are considered inputs of the fuzzy logic system. Meanwhile, traffic light states and nodes' information are used to tune the output fuzzy membership functions via reinforcement learning algorithm. The efficiency of our scheme in controlling ambiguity and uncertainty of the vehicular environment is confirmed through simulations in various vehicle densities and different traffic lights duration. Simulation results show our HIFS scheme's superiority over the state-of-the-art methods in terms of delivery ratio, average delay, path length, and routing overhead.