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首页> 外文期刊>Mathematical research letters: MRL >AI-Enabled Reliable Channel Modeling Architecture for Fog Computing Vehicular Networks
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AI-Enabled Reliable Channel Modeling Architecture for Fog Computing Vehicular Networks

机译：用于雾计算车辆网络的启用AI的可靠频道建模架构

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摘要

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.
机译：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命日常人类生活中起着显着作用。 FOG无线电接入网络正在高可靠性的实时交互式应用程序的数十亿无线设备。今天的车辆网络中的一个关键挑战是缺乏具有更好的乘客服务质量（QoS）的标准无线信道模型，同时享受愉快的旅行（即，高度可视化的视频，图像，新闻，给朋友/亲属的电话呼叫）。为了解决这些问题，本文以四种方式大大贡献。首先，我们开发了一种基于AI的基于AI的可靠和无干扰的移动性管理算法（RIMMA），用于雾计算车辆内网络，因为交通监控和驾驶员的安全管理是重要的，基本的基础。与FC相关的提议的RIMMA显着提高了计算，通信，合作和存储空间。此外，在高度移动车辆中有效地监测其自适应，可靠，智能，移动性的自适应和移动性感知性质和零星内容。其次，我们提出了一种可靠和延迟宽容的无线频道模型，具有更好的乘客乘客。第三，我们提出了一种新颖的可靠和有效的多层雾驱动的车辆内部框架。第四，我们在移动性，可靠性和丢包比方面优化QoS。此外，将所提出的RIMMA与现有的竞争传统方法（即基线）进行比较。实验结果表明，建议的RIMMA优于城域网网络传统技术。

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