Artificial intelligence (AI)-driven fog computing (FC) and its emerging role in vehicular networks is playing a remarkable role in revolutionizing daily human lives. Fog radio access networks are accommodating billions of Internet of Things devices for real-time interactive applications at high reliability. One of the critical challenges in today's vehicular networks is the lack of standard wireless channel models with better quality of service (QoS) for passengers while enjoying pleasurable travel (i.e., highly visualized videos, images, news, phone calls to friends/relatives). To remedy these issues, this article contributes significantly in four ways. First, we develop a novel AI-based reliable and interference-free mobility management algorithm (RIMMA) for fog computing intra-vehicular networks, because traffic monitoring and driver's safety management are important and basic foundations. The proposed RIMMA in association with FC significantly improves computation, communication, cooperation, and storage space. Furthermore, its self-adaptive, reliable, intelligent, and mobility-aware nature, and sporadic contents are monitored effectively in highly mobile vehicles. Second, we propose a reliable and delay-tolerant wireless channel model with better QoS for passengers. Third, we propose a novel reliable and efficient multi-layer fog driven inter-vehicular framework. Fourth, we optimize QoS in terms of mobility, reliability, and packet loss ratio. Also, the proposed RIMMA is compared to an existing competitive conventional method (i.e., baseline). Experimental results reveal that the proposed RIMMA outperforms the traditional technique for intercity vehicular networks.

中文翻译：

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雾计算车载网络的AI启用可靠通道建模架构

人工智能（AI）驱动的雾计算（FC）及其在车辆网络中的新兴作用在革新人们的日常生活中起着举足轻重的作用。雾无线电接入网络正在以高可靠性容纳数十亿个用于实时交互应用的物联网设备。当今车载网络的关键挑战之一是缺乏在享受愉快旅行的同时（为乘客提供高可视化的视频，图像，新闻，打给朋友/亲戚的电话）为乘客提供更好的服务质量（QoS）的标准无线信道模型。为了解决这些问题，本文通过四种方式做出了重要贡献。首先，我们开发了一种新颖的基于AI的可靠且无干扰的移动性管理算法（RIMMA），用于雾计算车内网络，因为交通监控和驾驶员的 安全管理是重要而基础的基础。提议的RIMMA与FC关联可显着改善计算，通信，协作和存储空间。此外，在高度机动的车辆中，其自适应，可靠，智能和移动感知的特性以及零星的内容得到了有效监控。其次，我们为乘客提供了一个具有更好QoS的可靠且耐延迟的无线信道模型。第三，我们提出了一种新颖的，可靠的，高效的多层雾驱动的车辆间框架。第四，我们在移动性，可靠性和丢包率方面优化了QoS。此外，将提议的RIMMA与现有的竞争性常规方法（即基准）进行了比较。实验结果表明，所提出的RIMMA优于传统的城际车辆网络技术。