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The Internet of vehicles (IoV) is a large information interaction network that collects information on vehicles, roads and pedestrians. One of the important uses of vehicle networks is to meet the entertainment needs of driving users through communication between vehicles and roadside units (RSUs). Due to the limited storage space of RSUs, determining the content cached in each RSU is a key challenge. With the development of 5G and video editing technology, short video systems have become increasingly popular. Current widely used cache update methods, such as partial file precaching and content popularity- and user interest-based determination, are inefficient for such systems. To solve this problem, this paper proposes a QoE-driven edge caching method for the IoV based on deep reinforcement learning. First, a class-based user interest model is established. Compared with the traditional file popularity- and user interest distribution-based cache update methods, the proposed method is more suitable for systems with a large number of small files. Second, a quality of experience (QoE)-driven RSU cache model is established based on the proposed class-based user interest model. Third, a deep reinforcement learning method is designed to address the QoE-driven RSU cache update issue effectively. The experimental results verify the effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

To meet the execution requirements of delay-sensitive services in vehicular edge computing (VEC) networks, vehicular services need to be offloaded to edge computing nodes. For complex, large-scale services, the services need to be migrated if the services are not completed before the vehicles leave the coverage of edge computing nodes. Trust and resource matching between areas thus become major problems. This paper studies the decision model of vehicular service offloading and migration. First, software-defined network (SDN) technology is introduced into the traditional network architecture, and a two-layer distributed SDN-controlled VEC network architecture is designed, which is divided into a domain control layer and an area control layer. In this framework, we use the consortium blockchain as a carrier to share network topology information between SDN controllers to prevent information leakage. We then established a service offloading and migration optimization problem model to minimize service execution delay, reduce energy consumption and maximize the throughput of the blockchain system. We describe the problem model as a Markov Decision Process (MDP), introduce a deep reinforcement learning (DRL) algorithm named asynchronous advantage actor-critic (A3C) and design a dynamic service offloading and migration algorithm (DSOMA) based on A3C to solve the problem. Simulation results show that DSOMA can increase the throughput of the blockchain system, and DSOMA is superior to the deep Q-learning (DQN) algorithm and greedy offloading algorithm in reducing service execution delay and system energy consumption. [ABSTRACT FROM AUTHOR]

Online Ride-Hailing(ORH) service enables riders to enjoy on-demand transportation service with mobile devices. Despite the convenience of the service, privacy leakage could be caused because riders need to submit their locations during ride matching. To protect users’ privacy, much effort has been done to construct a privacy-enhanced ORH system in recent years. However, almost all of current privacy-preserving schemes match the nearest driver to the requesting rider, it is not the optimal matching strategy and unnecessary waste of travel distance may be caused in this way. To solve the problem, we propose a privacy-preserving ORH scheme named pRide, which minimizes the overall travel distance by matching the best driver in global perspective instead of the nearest driver in the local region. To find the best driver, we utilize a deep learning model to predict emergence of ride requests in various regions, and enables the ORH server to perform ride matching leveraging prediction results. Based on the framework, we also propose optimizations to enhance pRide. Moreover, to further defend inference attacks from riders, we propose a private comparing algorithm at the cost of a little communication overhead. Through theoretical analysis and experiments, we prove that pRide is able to decrease the overall travel distance securely and efficiently in ORH matching scenarios. [ABSTRACT FROM AUTHOR]