【Author】 Aloqaily, Moayad; Al Ridhawi, Ismaeel; Guizani, Mohsen
【Source】IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
【Abstract】The aerial capabilities and flexibility in movement of Unmanned Aerial Vehicles (UAVs) has enabled them to adaptively provide both traditional and more contemporary services. In this article, we introduce a solution that integrates the capabilities of both UAVs and Unmanned Ground Vehicles (UGVs) to provide both intelligent connectivity and services to both aerial and ground connected devices. A cooperative solution is adopted that considers nodes' power and movement constraints. The UAV and UGV cooperative process ensures continuous power availability to UAVs to support seamless and continuous service availability to end-devices. A Federated Learning (FL) approach is adopted at the edge to ensure accurate and up-to-date service provisioning in accordance with the surrounding environment and network constraints. Moreover, Blockchain technology is used to decentralize the provisioning and control aspects, and ensure authenticity and integrity. Extensive simulations are conducted to test the soundness and applicability of the proposed solution. Results show significant improvement in terms of connectivity, service availability, and UAV energy enhancements when compared to traditional mobile and vehicular communication techniques.
【Keywords】Task analysis; Performance evaluation; Blockchains; Virtualization; Unmanned aerial vehicles; Service level agreements; Servers; Unmanned aerial vehicle; unmanned ground vehicle; artificial intelligence; blockchain; federated learning
【标题】能源感知区块链和联邦学习支持的车载网络
【摘要】无人驾驶飞行器 (UAV) 的空中能力和移动灵活性使它们能够自适应地提供传统和更现代的服务。在本文中,我们介绍了一种集成无人机和无人地面车辆 (UGV) 功能的解决方案,可为空中和地面连接设备提供智能连接和服务。采用考虑节点功率和移动约束的协作解决方案。 UAV 和 UGV 协作流程确保 UAV 的持续电力可用性,以支持终端设备的无缝和持续服务可用性。在边缘采用联邦学习 (FL) 方法,以确保根据周围环境和网络限制提供准确和最新的服务。此外,区块链技术用于分散供应和控制方面,并确保真实性和完整性。进行了广泛的模拟以测试所提出解决方案的可靠性和适用性。结果表明,与传统的移动和车载通信技术相比,在连接性、服务可用性和无人机能量增强方面有显着改进。
【关键词】任务分析;绩效评估;区块链;虚拟化;无人机;服务等级协定;服务器;无人驾驶的航空机;无人驾驶地面车辆;人工智能;区块链;联邦学习
【收录时间】2022-07-06
【文献类型】Article; Early Access
【论文大主题】区块链联邦学习
【论文小主题】两者结合
【影响因子】9.551
【翻译者】石东瑛
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