论文标题
区块链辅助5G-UAV网络的设计指南
Design Guidelines for Blockchain-Assisted 5G-UAV Networks
论文作者
论文摘要
第五代(5G)无线网络旨在通过高数据速率(通常是GBPS订单)和低潜伏期来满足各种最终用户服务质量(QoS)。再加上雾气和移动边缘计算(MEC),5G可以达到高数据速率,从而实现了复杂的自主智能城市服务,例如自动驾驶汽车的大型部署和大型人工智能(AI)支持工业制造。但是,为了满足低和高度密集的位置中连接的IoT设备的数量呈指数增长,以及不规则的数据和服务请求,通过固定和昂贵的基本站支持传统单元格的过程需要重新启动以多样化的智能城市场景的形式,以无人驾驶汽车(无人驾驶汽车)形式启用按需移动访问点。本文设想了一个5G网络环境,该环境由启用区块链的无人机支持,以满足用网络访问供应的动态用户需求。该解决方案使分散的服务交付(无人机作为服务)并以可靠且安全的方式往返最终用户。公共和私人区块链都在无人机内部部署,并由雾和云计算设备和数据中心支持,以提供广泛的复杂身份验证的服务和数据可用性。特别关注数据传递的数据传递率和消息交换中,针对传统的无UAV支持的蜂窝网络。还讨论了有关新兴技术(例如联合学习)的挑战和未来的研究。
Fifth Generation (5G) wireless networks are designed to meet various end-user Quality of Service (QoS) requirements through high data rates (typically of Gbps order) and low latencies. Coupled with Fog and Mobile Edge Computing (MEC), 5G can achieve high data rates, enabling complex autonomous smart city services such as the large deployment of self-driving vehicles and large-scale Artificial Intelligence (AI)-enabled industrial manufacturing. However, to meet the exponentially growing number of connected IoT devices and irregular data and service requests in both low and highly dense locations, the process of enacting traditional cells supported through fixed and costly base stations requires rethought to enable on-demand mobile access points in the form of Unmanned Aerial Vehicles (UAV) for diversified smart city scenarios. This article envisions a 5G network environment that is supported by blockchain-enabled UAVs to meet dynamic user demands with network access supply. The solution enables decentralized service delivery (Drones as a Service) and routing to and from end-users in a reliable and secure manner. Both public and private blockchains are deployed within the UAVs, supported by fog and cloud computing devices and data centers to provide wide range of complex authenticated service and data availability. Particular attention is paid tocomparing data delivery success rates and message exchange in the proposed solution against traditional UAV-supported cellular networks. Challenges and future research are also discussed with highlights on emerging technologies such as Federated Learning.