Blockchain-Based Intelligent Trusted Computational Resource Allocation for Low-Altitude Networks

In low-altitude networks, unmanned aerial vehicles (UAVs) can offer services such as logistics, intelligence surveillance, and environmental monitoring, aided by base stations (BSs) with substantial computational resources. However, BSs must defend against malicious UAVs that may overload resources or launch denial-of-service attacks. In this paper, we formulate a blockchain-enabled access control model, which uses the UAV identities (IDs) and trajectories, positive and negative interactions with the BS to evaluate the reputations of UAVs. In the blockchain, the elected miner generates blocks containing UAV IDs, coordinates, interactions, and reputation values. To defend against malicious UAVs, this paper formulates a trusted computational resource allocation optimization problem, solved by safe reinforcement learning (RL) with a three-level hierarchical structure. Specifically, this method uses the designed structure to optimize the BS access control, resource allocations, and block size. In particular, we design an E-network to evaluate the long-term risk resulting from the chosen policy, which is used to refine the policy distribution for safe exploration. A modified reward function accounts for immediate risks, preventing short-term dangerous explorations that could lead to illegal access or computational failures. We prove the Lyapunov asymptotic stability of the proposed system and derive the reward upper bound. Simulation results show that our scheme can converge to the upper bound, outperform the benchmark, and validate the effectiveness via ablation experiments.