Broadcast of beacon messages including geographic coordinates, node speeds, and directions are among the most commonly used methods in routing protocols of VANETs to obtain neighboring positions. Broadcast of periodic beacon messages in fixed time intervals will reduce network performance due to increased channel load and contention. In this paper, an adaptive update strategy for sending beacon messages according to the VANETs' characteristics (position, speed, and direction) and the nature of broadcast wireless channel in an opportunistic routing strategy is studied. It is based on two rules: 1) an estimation of the lifetime of the links between vehicles' beacon messages are sent after the expiration of the estimated time to inform their local topology and 2) if the forwarding set of consecutively received data packets is changed, a beacon message is sent to maintain the accuracy of the topology. The simulation results show that the proposed strategy significantly reduces the cost of routing and improves network performance in terms of packet-delivery ratios, average end-to-end delay, and routing overhead.

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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.

No abstract

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.