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.

Advanced communication and information technologies enable smart grids to be more intelligent and automated, although many security issues are emerging. Security situational awareness (SSA) has been envisioned as a potential approach to provide safe services for power systems’ operation. However, in the power cloud master station mode, massive heterogeneous power terminals make SSA complicated, and failure information cannot be promptly delivered. Moreover, the dynamic and continuous situational space also increases the challenges of SSA. By taking advantages of edge intelligence, this paper introduces edge computing between terminals and the cloud to address the drawbacks of the traditional power cloud paradigm. Moreover, a deep reinforcement learning algorithm based on the edge computing paradigm of multiagent deep deterministic policy gradient (MADDPG) is proposed. The minimum processing cost under the premise of minimum detection error rate is taken to analyze the smart grids’ SSA. Performance evaluations show that the algorithm under this paradigm can achieve faster convergence and the optimal goal, namely the provision of real-time protection for smart grids.

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.

Edge computing (EC) is an essential component of large-scale intelligent manufacturing systems for Industry 4.0, which promises to provide a preprocessing platform for the massive data generated by the terminals and guarantee lower delay and more security compared to directly processing data in cloud computing. Nevertheless, access authentication is a crucial security issue of current EC systems, and, thus, this article presents a solution to enhance the access classification accuracy by exploiting the physical layer information. Our method employs a weighted voting scheme for channel state information based authentication using a single sample which includes sample segmentation, grouping, and weighted voting and finally achieves the fast and low complexity secure-access requirement of the EC system without increasing the individual devices’ sample size and computational complexity. Experimental results utilizing public datasets and field-measured datasets demonstrate that the proposed weighted voting method has higher accuracy and robustness than existing methods.

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.

Multi-tier computing (MC) utilizes computing resources from the cloud, fog, edge, and end layers to promote intelligent Internet of Things (IoT) applications. Federated learning (FL) in MC offers a prospective distributed and privacy-preserving framework to deploy deep learning applications in different layers. Due to time-varying network topologies, wireless channel states, and computational workloads, MC faces a dynamic and uncertain environment, which poses additional challenges to FL task processing. In this paper, we propose a novel adaptive training and aggregation federated learning (ATAFL) framework. Specifically, local model training can be performed at end devices, edge nodes, and fog nodes. The global aggregator can choose from the edge, fog, and cloud layers. A joint optimization problem of training, aggregation node selection, and resource allocation is further formulated to minimize system latency and energy consumption. Moreover, digital twin and deep reinforcement learning (DRL) techniques are integrated into the MC network to design optimal node selection and resource allocation strategies based on captured state information of the MC system. We implement a prototype, and experimental results show that our proposed DRL-based algorithm reduces system latency and energy consumption compared with the benchmark algorithms.