mlsanityData to 7 Oct 2026

Blockchain-Enabled Routing for Zero-Trust Low-Altitude Intelligent Networks

arXiv 2602.23667 ↗First posted 27 Feb 2026cs.NI cs.AIcited by 0

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Abstract (arXiv)

Due to the scalability and portability, low-altitude intelligent networks (LAINs) are essential in various fields such as surveillance and disaster rescue. However, in LAINs, unmanned aerial vehicles (UAVs) are characterized by the distributed topology and high mobility, thus vulnerable to security threats, which may degrade routing performances for data transmissions. Hence, how to ensure the routing stability and security of LAINs is challenging. In this paper, we focus on the routing with multiple UAV clusters in LAINs. To minimize the damage caused by potential threats, we present the zero-trust architecture with the software-defined perimeter and blockchain techniques to manage the identify and mobility of UAVs. Besides, we formulate the routing problem to optimize the end-to-end (E2E) delay and transmission success ratio (TSR) simultaneously, which is an integer nonlinear programming problem and intractable to solve. Therefore, we reformulate the problem into a decentralized partially observable Markov decision process. We design the multi-agent double deep Q-network-based routing algorithms to solve the problem, empowered by the soft-hierarchical experience replay buffer and prioritized experience replay mechanisms. Finally, extensive simulations are conducted and the numerical results demonstrate that the proposed framework reduces the average E2E delay by 59% and improves the TSR by 29% on average compared to benchmarks, while simultaneously enabling faster and more robust identification of low-trust UAVs.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 16 of its 69 references that are in the map. First posted on arXiv 27 Feb 2026.

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16 of 69 references.

References · 16

  1. 2015Deep Reinforcement Learning with Double Q-learningcited by 1,704
  2. 2015Prioritized Experience Replaycited by 1,070
  3. 2016Deep Reinforcement Learning for Robotic Manipulation with Asynchronous Off-Policy Updatescited by 342
  4. 2021Applications of Multi-Agent Reinforcement Learning in Future Internet: A Comprehensive Surveycited by 34
  5. 2022A Secure and Intelligent Data Sharing Scheme for UAV-Assisted Disaster Rescuecited by 9
  6. 2025A Weighted Byzantine Fault Tolerance Consensus Driven Trusted Multiple Large Language Models Networkcited by 9
  7. 2023Dynamic Routing for Integrated Satellite-Terrestrial Networks: A Constrained Multi-Agent Reinforcement Learning Approachcited by 8
  8. 2025Distributionally Robust Optimization for Aerial Multi-access Edge Computing via Cooperation of UAVs and HAPscited by 5
  9. 2023Routing Recovery for UAV Networks with Deliberate Attacks: A Reinforcement Learning based Approachcited by 3
  10. 2025Joint Computation Offloading and Resource Allocation for Uncertain Maritime MEC via Cooperation of UAVs and Vesselscited by 3
  11. 2023ESCM: An Efficient and Secure Communication Mechanism for UAV Networkscited by 2
  12. 2025Rotatable Antenna-Enabled Wireless Communication: Modeling and Optimizationcited by 1
4 more
  1. 2025Towards Zero Trust Security in Connected Vehicles: A Comprehensive Surveycited by 1
  2. 2025Trusted Routing for Blockchain-Enabled Low-Altitude Intelligent Networkscited by 1
  3. 2025Trusted Routing for Blockchain-Empowered UAV Networks via Multi-Agent Deep Reinforcement Learningcited by 1
  4. 2025Hierarchical Task Offloading and Trajectory Optimization in Low-Altitude Intelligent Networks Via Auction and Diffusion-based MARLcited by 1

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