With the rapid development of the Internet of Things (IoT), the growth of the Industrial Internet of Things (IIoT) applied in the industrial sector has also been swift. However, in practical applications, there are still issues such as the misalignment between theory and application, the lack of a unified standardization framework, and the frequent occurrence of data silos. These issues limit the maintainability and scalability of IIoT systems and increase the digitalization costs for enterprises. Based on this, drawing from the design principles of classic general-purpose operating systems, we propose the concept of an IoT operating system platform. As a software infrastructure aimed at the next generation of IIoT, the IoT OS platform consists of general-purpose computer operating systems and the platform software running on them. It manages computing resources and entities such as sensors, networks, and ubiquitous artificial intelligence applications or systems, and provides service support for IIoT applications upwards, aiming to improve existing issues and enhance the specificity and scalability of IIoT systems. This paper presents the current status of IIoT systems, the definition and architecture of the IoT OS platform, and validates the theoretical architecture through specific cases.

Accurate long-term cloud demand forecasting is critical for optimizing resource procurement and cost management in cloud computing, yet it remains challenging due to dynamic demand trends, limited historical data, and the poor generalization of existing models in few-shot scenarios. This paper proposes DimAug-TimesFM, a dimension-augmented framework for long-term cloud demand forecasting, which addresses these challenges through two key innovations. First, Delivery Period Extracting identifies critical resource delivery phases by analyzing smoothed utilization trends and differencing thresholds, enabling focused modeling on periods reflecting actual demand. Second, Dimension-Augmented TimesFM enhances the pretrained TimesFM model by integrating cross-pool data via Dynamic Time Warping based similarity matching, enriching training data while mitigating distribution discrepancies. Experiments on real-world cloud resource utilization data demonstrate that DimAug-TimesFM significantly outperforms SOTA baselines (e.g., TimesFM, DLinear, PatchTST) in both short-term (16-day) and long-term (64-day and 128-day) forecasting tasks, achieving average reductions 72.9–81.7% in RMSE. DimAug-TimesFM also exhibits better robustness in scenarios where TimesFM fails, attributed to its synergistic integration of temporal feature enhancement and cross-pool data augmentation. This work provides a practical solution for few-shot cloud demand forecasting, enabling enterprises to align resource allocation with dynamic usage patterns and reduce operational costs.

Federated learning (FL) encounters significant challenges in heterogeneous client environments, due to statistical heterogeneity affecting the global model’s generalization. To address these issues, we introduce FedGAC, an efficient adaptive personalized FL (pFL) method focused on critical learning periods (CLP). Unlike existing methods that assume all training stages are equally critical and require participation from all clients, FedGAC strategically select clients during CLP to reduce computational and communication burdens. To enhance the global model’s generalization across statistically diverse clients, we integrate an Adaptive Initialization of Local Models (AILM) module with a personalized aggregation strategy. Training parameters are dynamically adjusted according to dataset quality, ensuring both accuracy and efficiency. Additionally, FedGAC employs parameter compression techniques to further reduce communication and computational costs. We validate the effectiveness of FedGAC through extensive experiments on four benchmark datasets in computer vision. The results demonstrate that FedGAC significantly enhances both accuracy and communication efficiency compared to state-of-the-art FL methods. This makes FedGAC a promising approach for addressing heterogeneity in pFL. The code is available at https://github.com/buaaYYC/FedGAC.git.