arXiv 2508.009382025-07-31eess.SYcs.AIcs.CRcs.SY

基于多智能体深度强化学习的区块链赋能无人机网络可信路由

Trusted Routing for Blockchain-Empowered UAV Networks via Multi-Agent Deep Reinforcement Learning

College of Electronic and Information Engineering, Nanjing University of Aeronautics and Astronautics（电子与信息工程学院，南京航空航天大学）；State Key Laboratory of Integrated Service Networks, Xidian University（集成服务网络国家重点实验室，西安电子科技大学）；University of Houston（休斯顿大学）；Department of Computer Science and Engineering, Kyung Hee University（计算机科学与工程系，韩国庆熙大学）

Ziye Jia, Sijie He, Qiuming Zhu, Wei Wang, Qihui Wu, Zhu Han

备注：: IEEE Tcom Accepted

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AI总结：

针对无人机网络路由易受恶意攻击的问题，提出基于区块链信任管理机制和多智能体双重深度Q网络的路由算法，仿真显示时延较对比方法降低12.74%至16.6%。

摘要：

由于具有高灵活性和多功能性，无人机（UAV）被广泛应用于监视和灾难救援等领域。然而，在无人机网络中，由于分布式拓扑和高动态性，路由容易受到恶意破坏。因此，确保无人机网络的路由安全具有挑战性。在本文中，我们刻画了存在恶意节点的时变无人机网络中的路由过程。具体而言，我们将路由问题建模为最小化总时延的整数线性规划，该问题难以求解。然后，为解决网络安全问题，设计了一种基于区块链的信任管理机制（BTMM），用于动态评估信任值并识别低信任度的无人机。为了改进区块链中传统的实用拜占庭容错算法，我们提出了一种共识无人机更新机制。此外，考虑到局部可观测性，将路由问题重新表述为去中心化部分可观测马尔可夫决策过程。进一步，设计了一种基于多智能体双重深度Q网络的路由算法，以最小化总时延。最后，在存在受攻击无人机的情况下进行了仿真，数值结果表明，与多智能体近端策略优化算法、多智能体深度Q网络算法以及无BTMM的方法相比，所提机制的时延分别降低了13.39%、12.74%和16.6%。

英文摘要：

Due to the high flexibility and versatility, unmanned aerial vehicles (UAVs) are leveraged in various fields including surveillance and disaster rescue.However, in UAV networks, routing is vulnerable to malicious damage due to distributed topologies and high dynamics. Hence, ensuring the routing security of UAV networks is challenging. In this paper, we characterize the routing process in a time-varying UAV network with malicious nodes. Specifically, we formulate the routing problem to minimize the total delay, which is an integer linear programming and intractable to solve. Then, to tackle the network security issue, a blockchain-based trust management mechanism (BTMM) is designed to dynamically evaluate trust values and identify low-trust UAVs. To improve traditional practical Byzantine fault tolerance algorithms in the blockchain, we propose a consensus UAV update mechanism. Besides, considering the local observability, the routing problem is reformulated into a decentralized partially observable Markov decision process. Further, a multi-agent double deep Q-network based routing algorithm is designed to minimize the total delay. Finally, simulations are conducted with attacked UAVs and numerical results show that the delay of the proposed mechanism decreases by 13.39$\%$, 12.74$\%$, and 16.6$\%$ than multi-agent proximal policy optimal algorithms, multi-agent deep Q-network algorithms, and methods without BTMM, respectively.

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。