The increasing number of vehicles and dynamic changes in traffic situations make real-time route planning strongly necessary. The route-guiding method is supposed to cope with dynamic traffic situations. In addition, the ability to adapt to the second fastest route is very important when traffic congestion suddenly occurs on the fastest path. This paper proposes a method of using reinforcement learning to solve dynamic route planning problems, and the adaptation from a static learning rate to a dynamic learning rate enhances the capability to deal with emergent congestion. Meanwhile, the waiting time before each traffic light also is considered as a reward factor in the proposed algorithm. Contrast experiments have been conducted on the simulation network by SUMO, which has demonstrated well that our proposed method has better performance than other methods.

The edge access component of the Internet of Vehicles has a high computational rate and energy consumption. This paper proposes a distribution offloading algorithm based on deep Q-learning network (DQN) to achieve the best latency and sustainable scheduling. Firstly, the computational tasks of various vehicles are prioritized using the analytic hierarchy process (AHP) to assign different weights to the task processing rate in order to establish a relationship model. Secondly, by introducing edge computing based on DQN, the task offloading model is established by using the weighted sum of task processing rate as the optimization goal, which realizes the long-term utility of offloading strategies. The performance evaluation results show that, when compared to the Q-learning algorithm, the proposed method can reduce the average task processing delay by 17%, effectively improving the sustainable task offload efficiency.

SUMMARY With the rapid development of intelligent transportation, vehicle terminals generate a large number of data messages that need to be processed in real time, and the required computing and storage resources far exceed the load capacity of vehicle terminals. Mobile edge computing enables data resources to be processed near device terminals, and provides low‐latency and high‐reliability computing services to meet the power and service quality requirements of terminal devices. Therefore, in order to achieve better data resource management, this paper introduces mobile edge computing technology, and mainly researches secure message transmission optimization algorithms based on mobile edge computing. Firstly, we prioritize secure messages through the analytic hierarchy process. This can guarantee that the most urgent messages get the highest transmission level. Secondly, we establish an optimal task offloading model of delay and energy loss by assigning different weight factors to delay and energy loss. The Lagrangian relaxation method is used to transform the nonconvex problem into a convex problem. We use greedy algorithm to solve the main problem. Finally, the vehicle transmits secure messages through the topology of the local network within its defined communication range. Performance evaluation results show that the scheme not only reduces the redundant transmission of messages, but also improves the performance of end‐to‐end delay and message deliver success ratio of secure messages.