Accurately forecasting demand for intermittent items is essential for effective inventory control, improved service levels, and cost reduction. This study focuses on highly sparse, irregular, and volatile demand patterns and proposes a generalizable multi-source data-driven framework for intermittent demand forecasting, using automotive spare parts as a representative application scenario. The proposed framework integrates Transformer networks, multi-graph convolutional networks (GCNs), and a Mamba-based feature fusion module. The Transformer captures long-term temporal dependencies in historical demand sequences, while the multi-graph GCN incorporates prior knowledge—including traffic geography, socioeconomic indicators, and environmental attributes—to model spatial correlations across multiple supply nodes. The Mamba-based fusion module then integrates temporal and spatial features into a unified representation, enhancing predictive accuracy and robustness. Extensive experiments on real-world datasets of automotive spare parts in China show that the proposed framework exhibits competitive and often superior performance compared with TiDE, FSNet, Informer, and DLinear across multiple forecasting horizons (3-, 6-, and 9-step), as measured by RMSE, MAE, and $R^2$. The proposed approach provides a practical and adaptable solution for forecasting intermittent demand, offering valuable support for dynamic inventory management.

Deep learning (DL)-based physical-layer authentication (PLA) has shown promising performance in industrial Internet of Things (IIoT) scenarios. Existing methods formulate PLA as a single multiclassification task with a static training dataset and implicitly assume the training and authentication data are drawn from the same distribution. However, user mobility over time in mobile IIoT leads to a temporal distribution shift in channel impulse response (CIR), severely degrading authentication performance. To address this challenge, we propose a temporal meta-metric learning method for PLA (TMML-PLA), a temporal meta-metric learning method that reformulates multiuser PLA as a sequence of temporal few-shot classification tasks based on the intrinsic temporal correlation characteristic of CIR. When new CIR samples require authentication, the model dynamically constructs a task using a small set of the most recent CIR samples of each user as the support set to ensure distributional consistency. A temporal meta-metric learning scheme based on the prototypical network (ProtoNet) is introduced to tackle these tasks under evolving CIR distributions, and a temporal decay mechanism is designed to emphasize recent training samples. Experiments on a real-world IIoT dataset demonstrate that TMML-PLA significantly improves both accuracy and robustness. For instance, when the number of users is 15, and the authenticating CIR samples increase over time, TMML-PLA maintains stable accuracy from 88.20% to 85.75% while the DL-based ResNet drops sharply from 83.93% to 46.95%, confirming its robustness and accuracy in mobile IIoT scenarios.

In the past decade, the high computational overhead of asymmetric cryptography has remained a central challenge in end-to-end secure communication systems. To mitigate the performance bottlenecks inherent in the full SM2 encryption and decryption workflow, this paper introduces ParaSM2, a parallel restructuring optimization framework tailored for SM2-based cryptographic operations. ParaSM2 exploits the observed 2:1 processing ratio between KDF and HASH to perform cross-component parallel restructuring and applies fixed-prefix reuse together with dynamic task parallelism to eliminate 39.7% of redundant KDF computations. Furthermore, a vectorized reconstruction of the HASH message extension is incorporated to leverage SIMD parallel acceleration, thereby substantially enhancing throughput. Experimental evaluations against SM4-GCM and SM4-CBC on data blocks larger than 64 KB demonstrate that ParaSM2 achieves up to a 5.1× performance improvement on both x86 and ARM architectures, effectively reducing end-to-end latency and providing a scalable pathway for algorithmic optimization in cryptography across heterogeneous platforms.

Secure authentication and key agreement across heterogeneous trust domains is essential for reliable collaboration in the Industrial Internet. However, existing cross-domain AKA schemes still face certificate-management overhead, key-escrow risk, insufficient identity privacy, and high computational cost. To address these issues, this paper proposes LBCAK, a lightweight blockchain-assisted anonymous cross-domain AKA scheme. LBCAK combines a certificateless-style key construction with AuthLedger-based public-state coordination, where the blockchain maintains current registration states for cross-domain credential validation while online authentication and session-key establishment remain off-chain. Credential-state-specific protected identifiers are further used to reduce on-chain linkability without exposing raw identities or private credential materials. Security is analyzed under an eCK-style leakage model in the random-oracle setting and further examined using ProVerif, showing that LBCAK provides mutual authentication, identity privacy, untraceability, forward secrecy, and resistance to major active and key-leakage attacks. Performance evaluation shows that LBCAK reduces computational and communication overheads by 14.3% and 4.2%, respectively, demonstrating its practicality for resource-constrained Industrial Internet deployments.