In Agricultural Internet of Things (Ag-IoT), a large number of terminal sensor nodes with limited computational power and energy are widely deployed across various production sites to collect real-time data that support precision agriculture decision-making, which makes it challenging to perform complex computing tasks in real time. This article proposes an offline decision-making framework based on a compositional diffusion model that addresses the limitations of traditional offline deep reinforcement learning (DRL), which often struggles to produce high-quality policies in the presence of malicious-node interference and attacks and with insufficient decision trajectories in agricultural settings. Specifically, the proposed scheme leverages the diffusion model’s reverse generative process to synthesize numerous high-quality augmented samples from a small set of existing decision trajectories, which are then used to train robust policies, mitigating performance degradation in offline DRL due to limited trajectories and adversarial state perturbations introduced by malicious nodes. Experimental results demonstrate that the proposed method improves the quality and generalization of task offloading and resource allocation decisions, exhibiting superior stability and performance even under data sparsity and adversarial attacks.

This article explores the issue of fault-tolerant optimal pursuit strategies in a nonlinear pursuit-evasion (PE) game involving multiple pursuers and a single evader. The main challenge lies in ensuring the successful capture of the evader despite the presence of actuator partial loss of effectiveness and bias faults within the pursuers group. To overcome this challenge, a two-layer control architecture is proposed. At the control layer, an integral sliding-mode controller is developed to mitigate the impact of bias faults, and an adaptive estimation mechanism is incorporated to identify the fault parameters. At the decision-making layer, performance index functions for both the pursuers and the evader are formulated based on the PE state error and their respective control strategies, and optimal pursuit and evasion strategies are derived by solving the associated Hamilton–Jacobi–Isaacs (HJI) equations. Furthermore, adaptive dynamic programming (ADP) is employed, with each participant using a critic network to approximate the optimal strategies for the pursuers and the evader. The proposed control mechanism is theoretically proven to ensure that all closed-loop signals remain uniformly ultimately bounded, allowing the faulty pursuers to successfully capture the evader. Finally, the effectiveness of the approach is validated through two simulation examples.

As security issues facing the industrial Internet of Things (IIoT) continue to emerge, industrial organizations are working to further improve the security system. Zero trust (ZT) is seen as the future of industrial security, with a rising voice, but currently, no concrete implementation technique is available. In this article, we start with the requirements of ZT security and attempt to design a ZT technical framework applicable to wireless IIoT. Specifically, a three-step ZT security framework is proposed that builds on the benefits of physical-layer security to enhance ZT in IIoT. Security zone formation is done first, which then facilitates a trusted environment for subsequent device authentication and cryptographic negotiation. By integrating physical-layer security, several promising techniques, including artificial noise, physical fingerprint, and key distribution, are well designed to accomplish the proposed framework. Our analysis reveals that the proposed framework and the designed particular implementation techniques are feasible to enhance ZT security in wireless IIoT.

Residing in the proximity of end devices, edge computing (EC) holds great potential to provide low-latency, energy-efficient, and secure services, which has become an essential part of the Industrial Internet of Things (IIoT). To future accelerate task processing and reduce service latency, this work proposes an online incentive-driven task allocation scheme to stimulate collaborative computing among EC servers and IIoT devices. To better serve dynamic and heterogeneous tasks in terms of profiles and importance, EC servers (including neighboring servers) and IIoT devices with available resources can cooperatively process the tasks. Considering the heterogeneity of computing resources in edge servers and industrial IoT devices, we formulate a task allocation problem, which is NP hard. An online incentive-driven task allocation algorithm is proposed to this NP-hard problem, which will optimize task assignment strategies to maximize system utility, promote faster computing, and stimulate collaborative computing. Theoretical analyses show that the online incentive algorithm can satisfy incentive compatibility, individual rationality, computational efficiency, and feasibility. The results demonstrate that the proposed task allocation scheme with collaborative EC achieves superior performance and effectiveness.

The introduction of information and communication technologies makes network environments increasingly open, leaving smart-grid control systems incredibly vulnerable to malicious attacks. False data injection (FDI) attacks stealthily tamper with measurement data, resulting in erroneous decisions made by the control center that greatly influence the normal operation of the power system. By taking advantage of real-time data acquisition with edge computing, in this article, we propose a scheme based on classification of predicted residuals (CPRs) for the FDI attack detection. The CPR scheme first predicts the acquired measurement data at the edge of the sensing network via developing an accurate prediction model. Followed the novel real-time classification method under the edge devices supporting, it classifies the predicted residuals independent of the false data to enhance the detection accuracy. Through these two steps, the detection rate of FDI attacks is greatly improved. The proposed scheme is validated in a real microgrid testbed. Experimental results show that the CPR scheme performs well in detecting FDI attacks and remains sensitive in injection attack probability and magnitude. The detection scheme even has effectiveness at low injection attack probability and magnitude (5% and 0.018 per thousand, respectively). Furthermore, it also proves that the proposed scheme has applicability in high real-time requirements at the edge of smart grids.

In this article, a hierarchical task offloading strategy is presented for delay-tolerant and delay-sensitive missions by integrating edge computing and artificial intelligence into Cybertwin-based network to guarantee user Quality of Experience (QoE), low latency, and ultrareliable services, which are huge challenges to the Internet of Things (IoT) due to diverse application requirements, heterogeneous multidimensional resources, and time-varying network environments. The novel scheme achieves faster task processing, dynamic real-time allocation, and lower overhead by taking advantages of a multiagent deep deterministic policy gradient (MADDPG). Moreover, federated learning is used to train the MADDPG model. Numerical results demonstrate that the proposed algorithm improves system processing efficiency and task completion ratio compared to the benchmark schemes.

Recent years, various blind image quality assessment (BIQA) methods based on deep neural network have been proposed and achieved excellent performance. Most existing deep BIQA methods learn a regression model from distorted images with corresponding human subjective scores with end-to-end neural networks. However, such schemes ignore the characteristics of human visual system (HVS) since human beings are the ultimate receivers of the images. This paper proposed a dual-channel deep neural architecture for BIQA, which incorporated the visual sensitivity with taken the psychophysical characteristics of human visual system (HVS) into consideration. Furthermore, a new loss function is employed, which penalizes the deep network when the order of prediction scores is different from the ground truth order. The experimental results on two benchmark IQA databases show that the proposed method outperforms the state-of-the-arts.