UAV-assisted Internet of Vehicles: A Framework Empowered by Reinforcement Learning and Blockchain
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Abstract (arXiv)
This paper addresses the challenges of selecting relay nodes and coordinating among them in UAV-assisted Internet-of-Vehicles (IoV). The selection of UAV relay nodes in IoV employs mechanisms executed either at centralized servers or decentralized nodes, which have two main limitations: 1) the traceability of the selection mechanism execution and 2) the coordination among the selected UAVs, which is currently offered in a centralized manner and is not coupled with the relay selection. Existing UAV coordination methods often rely on optimization methods, which are not adaptable to different environment complexities, or on centralized deep reinforcement learning, which lacks scalability in multi-UAV settings. Overall, there is a need for a comprehensive framework where relay selection and coordination are coupled and executed in a transparent and trusted manner. This work proposes a framework empowered by reinforcement learning and Blockchain for UAV-assisted IoV networks. It consists of three main components: a two-sided UAV relay selection mechanism for UAV-assisted IoV, a decentralized Multi-Agent Deep Reinforcement Learning (MDRL) model for autonomous UAV coordination, and a Blockchain implementation for transparency and traceability in the interactions between vehicles and UAVs. The relay selection considers the two-sided preferences of vehicles and UAVs based on the Quality-of-UAV (QoU) and the Quality-of-Vehicle (QoV). Upon selection of relay UAVs, the decentralized coordination between them is enabled through an MDRL model trained to control their mobility and maintain the network coverage and connectivity using Proximal Policy Optimization (PPO). The evaluation results demonstrate that the proposed selection and coordination mechanisms improve the stability of the selected relays and maximize the coverage and connectivity achieved by the UAVs.
Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 8 of its 49 references that are in the map. First posted on arXiv 22 Jan 2025.
Builds on
8 of 49 references.
References · 8
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- 2020PRIMAL2: Pathfinding via Reinforcement and Imitation Multi-Agent Learning -- Lifelongcited by 61
- 2020Joint Auction-Coalition Formation Framework for Communication-Efficient Federated Learning in UAV-Enabled Internet of Vehiclescited by 14
- 2020Multi-UAV Path Planning for Wireless Data Harvesting with Deep Reinforcement Learningcited by 10
- 2018Performance Evaluation of the Quorum Blockchain Platformcited by 3
- 2025Adaptive Target Localization under Uncertainty using Multi-Agent Deep Reinforcement Learning with Knowledge Transfercited by 3
- 2020Energy-efficient Deployment of Multiple UAVs Using Ellipse Clustering to Establish Base Stationscited by 1
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Citing papers per year: 2025: 3.
3 papers.
- 2025Comprehensive Review of Reinforcement Learning for Medical Ultrasound Imagingcited by 2
- 2025Hierarchical Federated Graph Attention Networks for Scalable and Resilient UAV Collision Avoidancecited by 1
- 2025End-to-End Framework Integrating Generative AI and Deep Reinforcement Learning for Autonomous Ultrasound Scanningcited by 0
- 2025Hierarchical Federated Graph Attention Networks for Scalable and Resilient UAV Collision Avoidance5 Nov 2025
- 2025End-to-End Framework Integrating Generative AI and Deep Reinforcement Learning for Autonomous Ultrasound Scanning31 Oct 2025
- 2025Comprehensive Review of Reinforcement Learning for Medical Ultrasound Imaging19 Mar 2025
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