The Internet of Things (IoT) technology and systems have penetrated every aspect of our lives and generated enormous economic benefits. At the same time, research on the data security of IoT systems has been one of the key topics in IoT fields. Network attacks and intrusions have become the main threats to the data security of the IoTs, which have become the main obstacles to the development and application of the IoTs. In this article, we propose an intrusions and attack detection model to ensure the data security of IoT systems by using the Transformer model and multiwavelets learning. Based on the architecture of IoT systems, we first proposed a multi-level intrusion detection model to detect attack data in the cloud layer and edge terminal layer. In this detection model, a Transformer model and discrete wavelet transform (DWT) based approach is proposed to ensure the effectiveness and accuracy of the model. To extract and make full use of frequency information of traffic data in an IoT network, we embed DWT technique and multiwavelets learning into the Transformer model to propose a novel DWT-based Transformer architecture, which achieves outstanding performance in detecting intrusion actions. Simulating on IoT system in laboratory environment, the proposed security prediction model achieves pretty good performance in predicting intrusion actions.

Vehicular fog computing (VFC) is an innovative computing paradigm with an exceptional ability to improve the vehicles’ capacity to manage computation-intensive applications with both low latency and energy consumption. Moreover, more and more Artificial Intelligence (AI) technologies are applied in task offloading on the Internet of Vehicles (IoV). Focusing on the problems of computing latency and energy consumption, in this paper, we propose an AI-based Vehicle-to-Everything (V2X) model for tasks and resource offloading model for an IoV network, which ensures reliable low-latency communication, efficient task offloading in the IoV network by using a Software-Defined Vehicular-based FC (SDV-F) architecture. To fit to time-critical data transmission task distribution, the proposed model reduces unnecessary task allocation at the fog computing layer by proposing an AI-based task-allocation algorithm in the IoV layer to implement the task allocation of each vehicle. By applying AI technologies such as reinforcement learning (RL), Markov decision process, and deep learning (DL), the proposed model intelligently makes decision on maximizing resource utilization at the fog layer and minimizing the average end-to-end delay of time-critical IoV applications. The experiment demonstrates the proposed model can efficiently distribute the fog layer tasks while minimizing the delay.

The study of Internet of Vehicle (IoV) based on Fog Computing (FC) and Artificial Intelligent (AI) has attracted more and more attention. However, there are still many problems require to investigate such as time-critical, scalability, load-balancing, energy consumption, and so on. Focusing on these problems, we proposed an AI-based Vehicle-to-Everything (V2X) model for tasks and resource offloading model for IoVs network, which ensures reliable low-latency communication efficient tasks offloading in IoV network by using Software Defined Vehicular based FC (SDV-F) architecture. To fit to time-critical data transmission task distribution, the proposed model reduces unnecessary task allocation at the fog computing layer by proposing an AI-based task-allocation algorithm in IoV layer to implement task allocation of each vehicle. By applying AI technologies of Reinforcement Learning (RL), Markov decision process, and Deep Learning (DL), the proposed model intelligently makes decision on maximizing resource utilization at the fog layer, and minimizing the average end-to-end delay of time-critical IoV applications. The experiment demonstrates the proposed model can efficiently distribute the fog layer tasks while minimizing the delay.

Steel manufacturing is a typical high-energy and high-carbon industry. Undetected tiny surface defects such as scratches and crazing lead to massive waste of steel raw materials and extra energy consumption in subsequent processing, while heavy detection models may increase the computational energy demand of on-site inspection systems. The purpose of this study is to develop a lightweight defect detection model that improves the recognition of weak-semantic and structurally distinctive surface defects while reducing computational cost for industrial deployment. To achieve this objective, we propose SA-DETR, a lightweight structure-aware defect detection model built on RT-DETR-L. Three targeted modules are introduced: O-EHGBlock retains fine-grained structural cues of micro-defects with reduced computation; Adaptive Structure-aware Query Selection (ASQS) supplements semantic query ranking with local structural information to improve the detection of weak-semantic defects; and lightweight StarC3 replaces redundant fusion blocks to cut computational overhead. Evaluated on NEU-DET and PCB datasets, SA-DETR boosts mAP@0.5 by 4.5 percentage points over RT-DETR-L, while reducing parameters by 36.0% and GFLOPs by 46.5%, with inference speed lifted to 103.0 FPS. These results demonstrate that SA-DETR provides a favorable trade-off between detection accuracy and computational efficiency, indicating its potential for resource-constrained industrial surface inspection and sustainable steel quality control.