Abstract
With the proliferation of mobile devices and a wealth of rich application services, the Internet of vehicles (IoV) has struggled to handle computationally intensive and delay-sensitive computing tasks. To substantially reduce the latency and the energy consumption, application work is offloaded from a mobile device to a remote cloud or a nearby mobile edge cloud for processing. Compared with remote clouds, mobile edge clouds are located at the edge of the network. Therefore, mobile edge computing (MEC) has the advantages of effectively utilizing idle computing and storage resources at the edge of the network and reducing the network transmission delay. In addition, mobile devices are increasingly moving toward intelligence. To satisfy the service experience and service quality requirements of mobile users, the vehicle Internet is transforming into the intelligent vehicle Internet. Artificial intelligence (AI) technology can adapt to rapidly changing dynamic environments to provide multiple task requirements for resource allocation, computational task scheduling, and vehicle trajectory prediction. On this basis, combined with MEC technology and AI technology, computing and storage resources are placed on the edge of the network to provide real-time data processing while providing more efficient and intelligent services. This article introduces IoV from three aspects, namely, MEC, AI and the advantages of combining the two, and analyzes the corresponding architecture and implementation technology. The application of MEC and AI in IoV is analyzed and compared with current approaches. Finally, several promising future directions in the field of IoV are discussed.
Highlights
With the rapid development of Internet of things (IoT) technology and the increasing number of vehicle networks, the traditional vehicle ad hoc network (VANET) is gradually being integrated into the Internet of vehicles (IoV)
We introduce the development of artificial intelligence (AI), analyze the relationship between AI and Deep reinforcement learning (DRL), discuss the theory and architecture of AI, and analyze the application of AI in IoV research
Using Q-learning or DQL algorithms can intelligently control the use of network resources in IoV [20].Reference [46] established a generic, green, intelligent, and scalable scheduling strategy for resource distribution, which is used to adapt to the randomness of the traffic environment, to learn from highdimensional input scheduling policies using the depth of the Q-network, to support the efficient operation of the vehicle network and balance the IoT gateway of the available energy, and to minimize the total cost
Summary
With the rapid development of Internet of things (IoT) technology and the increasing number of vehicle networks, the traditional vehicle ad hoc network (VANET) is gradually being integrated into the Internet of vehicles (IoV). IoV is a new model that combines VANETs and vehicle remote information processing to connect vehicles, people and things [1] It is a highly important field in intelligent transportation systems (ITSs), as it covers intelligent transportation, cloud computing, vehicle information services, logistics transportation services [2], [3], modern wireless technology, Internet access and communication and other technologies. In the emerging 5G network, the application of D2D (device to device) communication technology promises to substantially improve the spectrum efficiency to support data transmission between caching vehicles and mobile users [5]. In the age of IoV, vehicle-mounted intelligent modules can provide intelligent vehicle control, traffic management, accident prevention and navigation capabilities, along with rich multimedia and mobile Internet application services and many emerging interactive applications [12] that improve the user experience, reduce operating costs and promote a safe driving environment.
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