Uncrewed aerial vehicle (UAV) networks integrated with blockchain technology have been increasingly adopted to enable secure and decentralized coordination in distributed aerial systems. Within blockchain systems, the consensus mechanism plays a critical role in guaranteeing the consistency of shared data. However, the highly dynamic 3-D topology of UAV networks with unreliable wireless links can lead to consensus failure due to more frequent connectivity variations. In this article, the consensus node selection in a blockchain system becomes particularly critical. To tackle this problem, we develop a graph neural network (GNN)-based consensus node selection algorithm for blockchain-enabled UAV networks. GNN is particularly suitable for consensus stability prediction in highly dynamic UAV networks, as the UAV topology naturally forms a graph structure. To achieve a lightweight design, we only adopt relative angle, which is the most dominant factor in 3-D consensus stability proved by our theoretical analysis, as the edge feature in GNN. Simulation results demonstrate that GNN-based consensus node selection improves the consensus success rate by approximately 17.34%. Moreover, only incorporating the relative angle as an edge of GNN enables the proposed algorithm to achieve a near-optimal consensus success rate at low cost of computational overhead, with only 8.05-ms inference latency per consensus round.
Zixu Zhou, Xuefei Zhang, Yao Sun et al.· IEEE Internet of Things Jour...· 0 citations
In multi-controller Software-Defined Networking (SDN), Distributed Denial-of-Service (DDoS) attacks exhibit a"dispersed source, concentrated target"pattern across domains, i.e., attack traffic originates from multiple edge-controller domains but converges on a victim in a single aggregation controller domain. While entropy-based DDoS detectors are effective in single-controller settings, their direct application in multi-controller SDN reveals a previously overlooked anomaly. Through systematic experiments, we identify an aggregation bias: during the post-attack transition phase, the aggregation controller continues to generate excessive false positives, while edge controllers have already returned to normal. We attribute this phenomenon to the coupled effects of OpenFlow statistics lag and unconstrained dynamic-threshold drift. To address this issue, we propose a cross-domain confidence-fusion framework that leverages lightweight edge-side messages to calibrate aggregation-controller decisions without sharing raw traffic data. The framework is non-intrusive, communication-efficient, and incrementally deployable. Experiments on a three-controller linear Mininet testbed with 24 hosts over 10 runs show that the method preserves edge-controller performance while reducing the aggregation false positive rate from 8.87% to 1.96% and increasing the F1 score from 89.04% to 96.89%.
Developing autonomous hydraulic excavators is constrained by limited access to physical machines and the high cost of real-world experimentation. This paper proposes a simulation-to-real framework for learning a system-level digital surrogate using Long Short-Term Memory (LSTM) networks. Instead of modeling internal dynamics, the excavator is treated as an input-output operator, and the surrogate is trained to reproduce its closed-loop behavior under identical control inputs. The approach is first validated in a MuJoCo simulation environment and then transferred to a real excavator. To address measurement inconsistencies in real-world data, a consistency-aware state estimation method based on adaptive Kalman filtering is introduced. Experimental results demonstrate that the learned surrogate achieves high fidelity in both angular velocity and long-horizon trajectory reproduction under closed-loop autoregressive evaluation. These results confirm that the proposed model can serve as a drop-in surrogate for both simulation and physical systems, enabling scalable and efficient development of excavation automation algorithms.
Shuai-Tong Wang, Shen Wang, Qiang Wang et al.· 0 citations
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