Artificial Intelligence-Empowered Edge of Vehicles: Architecture, Enabling Technologies, and Applications
Artificial Intelligence-Empowered Edge of Vehicles: Architecture, Enabling Technologies, and Applications
复制标题
DOI：
10.1109/access.2020.2983609
复制
发表时间：
2020-03
期刊：
影响因子：
3.9
通讯作者：
Hongjing Ji;O. Alfarraj;Amr M. Tolba
中科院分区：
文献类型：
--
作者：
Hongjing Ji;O. Alfarraj;Amr M. Tolba
随着移动设备的激增和丰富的应用服务，车联网（IoV）已经在努力处理计算密集型和延迟敏感的计算任务。为了大幅减少延迟和能耗，应用程序工作从移动设备卸载到远程云或附近的移动边缘云进行处理。与远程云相比，移动边缘云位于网络的边缘。因此，移动边缘计算（MEC）具有有效利用网络边缘的空闲计算和存储资源，降低网络传输延迟的优点。此外，移动设备正日益向智能化方向发展。为满足移动用户对服务体验和服务质量的需求，车联网正在向智能车联网转型。人工智能（AI）技术能够适应快速变化的动态环境，为资源分配、计算任务调度和车辆轨迹预测提供多任务需求。在此基础上，结合MEC技术和AI技术，将计算和存储资源放置在网络边缘，提供实时数据处理的同时，提供更高效、更智能的服务。本文从MEC、AI以及两者结合的优势三个方面介绍了车联网，并分析了相应的架构和实现技术。分析了MEC和AI在车联网中的应用，并与现有方法进行了比较。最后，展望了车联网领域未来的发展方向。
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.