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

arXiv: https://arxiv.org/abs/2508.00938 · HTML: https://mlsanity.com/paper/2508.00938/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 54 references that are in the map.
- First posted on arXiv 31 Jul 2025.

## Builds on (most cited first, 5 of 5)

- [Deep Reinforcement Learning with Double Q-learning](https://mlsanity.com/paper/1509.06461/) (2015)
- [Hierarchical Aerial Computing for Internet of Things via Cooperation of HAPs and UAVs](https://mlsanity.com/paper/2202.06046/) (2022)
- [A Secure and Intelligent Data Sharing Scheme for UAV-Assisted Disaster Rescue](https://mlsanity.com/paper/2211.12988/) (2022)
- [Cooperative Cognitive Dynamic System in UAV Swarms: Reconfigurable Mechanism and Framework](https://mlsanity.com/paper/2405.11281/) (2024)
- [Routing Recovery for UAV Networks with Deliberate Attacks: A Reinforcement Learning based Approach](https://mlsanity.com/paper/2308.06973/) (2023)

## Built on by (most cited first, 1 of 1)

- [Blockchain-Enabled Routing for Zero-Trust Low-Altitude Intelligent Networks](https://mlsanity.com/paper/2602.23667/) (2026)

## Abstract (arXiv)

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.