Abstracts:Cloud-fog automation architecture has propelled the advancement of the Industrial Internet of Things (IIoT), significantly enhancing production efficiency and intelligence through extensive data collection and connectivity. Simultaneously, industrial cyber-physical system leverages this data to achieve intelligent control and optimization of production processes. As industrial production becomes increasingly specialized and complex, independent operations within a single domain are no longer sufficient to meet demands, making cross-domain collaborative production inevitable. Consequently, ensuring the security of cross-domain communication and data sharing has become a critical issue for IIoT under the cloud-fog automation architecture. Existing solutions encounter substantial management and computational burdens in cross-domain communication and data sharing, and they are vulnerable to privacy leakage risks. To address these challenges and enhance industrial production efficiency, this paper uses consortium blockchain to co-design a cross-domain authentication and data sharing scheme. The scheme ensures secure and private cross-domain communications with minimal computational, communication, and storage overhead. And, the proposed time-specific plaintext checkable encryption protocol can secure data during cross-domain sharing. Security and performance analyses show that the proposed scheme effectively reduces computational and communication resource demands while maintaining communication and data security.

Abstracts:Industrial Control Systems (ICSs) are the core of industrial production. Wireless technology, with its flexibility and adaptability, is catalyzing a transformative shift from traditional ICS to the advanced Industrial Wireless Control Systems (IWCSs). However, the openness of wireless media, high dynamics of the environment, and resource scarcity present unprecedented security challenges of high security defense costs and low detection inaccuracy for IWCS. State-of-the-art methods primarily treat ICS as a typical cyber-physical system, which focuses on security issues from the cyber and control domains, rather than the physical domain. As a result, they are unable to fully address the high dynamics of wireless channels and unknown attacks, ultimately failing to meet the stringent security requirements of industrial systems. To this end, this paper proposes a physical-domain whitelist as the final line of security defense leveraging the finite nature of the physical behavior space in industrial production systems. Moreover, a holistic cross-domain security-safety architecture is introduced, drawing inspiration from the integrated cyber-control-physical collaboration. In the proposed architecture, the top-down inherent security-safety defense and bottom-up risk backtracking form a close loop, which not only prevents unknown attacks but also facilitates rapid localization and response to attacks. In the experiment, the composite AGV scheduling control has been developed to verify the effectiveness of the architecture. Ultimately, the potential challenges of the cross-domain architecture for IWCS safety-security defense have been summarized.

Abstracts:In this paper, we focus on both the communication and sensing security in the proposed multiple-subcarrier (MC) non-orthogonal multiple access (NOMA)-assisted integrated sensing and communication (ISAC) systems, where an active eavesdropper is considered with imperfect channel state information (CSI). We consider a secure ISAC design by adopting the proposed secrecy rate and secrecy Cramér-Rao bound (S-CRB) metrics under the bounded CSI errors model. The joint design of artificial noise (AN) and dual-functional radar communications (DFRC) signals beamforming as well as subcarrier allocation is formulated to maximize the minimum achievable rate, while ensuring the hierarchical confidentiality requirements for users and satisfying the leakage CRB constraint for the target. To handle the non-convex problem, we devise a low-complexity successive convex approximation (SCA)-based suboptimal algorithm, and its $\varepsilon $ -optimality is validated via our proposed branch and bound (B&B) algorithm in simulations. Numerical results reveal the effectiveness and superiority of our proposed scheme compared to the other baselines. Moreover, the inherent win-win relationship between the sum secrecy rate and target S-CRB is demonstrated via various simulation results, which provides several insights into secure MC-NOMA ISAC network deployment.

Abstracts:In the era of smart cyber-physical grid, dynamic insecurity risk has become a significant concern due to the increasing integration of renewable energy sources and the inherent uncertainties in smart grid. Dynamic security assessment (DSA) has been adopted to hedge against such risks by estimating the stability of large-scale smart grids. Existing DSA approaches often involve complex high dimensional models which incur high communication and computational costs, hindering their practical adoption. In this paper, we address these limitations with the Quantum Federated Ensembled Variational Adaptive Learning (QFEVAL) approach for smart grid DSA. QFEVAL is designed to combine quantum machine learning and federated learning to handle the differential-algebraic equations that describe smart grid stability, providing an efficient way to deal with high-dimensional data and uncertainties. QFEVAL enables the training of the hybrid quantum-classical neural networks on distributed DSA datasets located at different nodes in smart grids, without requiring large numbers of parameters to be transmitted. QFEVAL accurately predicts the stability of the smart grid under various conditions, enabling the implementation of preventive stability control measures. Through extensive experiments, we demonstrate that QFEVAL achieves comparable performance to 9 state-of-the-art DSA approaches with more than 2 orders of magnitude fewer model parameter transmissions. QFEVAL paves the way for reliable, secure, and continuous electricity supply, offering a robust solution to the challenges of DSA in smart grids.

Abstracts:The Cloud-Fog Automation is a newly proposed digital industrial automation architecture aimed at accelerating the integration and collaboration of communication, computing, and control towards next-generation cyber-physical systems (CPSs). Data reliability is one of the key considerations for achieving Cloud-Fog Automation. Sensor nodes serve as infrastructures for data collection within industrial CPSs and are essential for maintaining ultra-high data reliability. However, the underlying sensor nodes communicate frequently, are damage-prone and difficult to identify, which dramatically shortens the network lifetime and poses great challenges to data reliability. Motivated by this fact, this paper co-designs communication architecture, algorithms, and computing models for next-generation industrial CPSs with Cloud-Fog Automation to ensure data reliability and functional security. First, a four-layer energy-efficient communication architecture is proposed and a cluster head computing algorithm based on double deep Q-learning (CH-DDQ) is designed inside the architecture. Besides, a 2-stage hyBrid fault detection scheme (2-Brain) is proposed for underlying sensor nodes. 2-Brain first incorporates the Obstacle Triple Jump Protocol (OTP) and OTP packets to improve hard fault detection performance. Then, an unsupervised sensor reading soft fault detection model (SR-SFD) based on contrastive learning, momentum, and tensor is adopted to learn discriminative representations of sensor readings and identify soft faults. Simulations and a case study in the nuclear power industry manifest CH-DDQ improves the network lifetime by 5.4%~484.3% compared to three peer methods, and OTP performs better than baselines by 33.1% on average. Additionally, SR-SFD exhibits high efficiency in sensor soft fault detection and other application scenarios.

Abstracts:Industrial Cyber-Physical Systems (ICPSs) have been motivating various Industry 4.0 endeavours, particularly with the integration of fog computing. Cloud-fog data caching paradigms, as supportive elements of ICPSs, have been adopted to cache user data, catering to diverse ICPS requirements such as data sensitivity and reduced access latency. In this hierarchical caching context, ensuring Cloud-Fog Data Integrity (CFDI) is crucial for maintaining the consistent functionality of ICPSs. Existing solutions primarily focus on examining the integrity of data cached solely on either cloud or fog nodes. However, cloud-cached data and fog-cached data are tightly coupled and should be considered simultaneously when checking data integrity. In this work, we introduce an over-the-air CFDI verification scheme, namely AirDIV, with a high accuracy and security guarantee. Instead of aggregating integrity proofs after proof transmission, AirDIV completes proof aggregation and transmission over the air for efficiency improvement. To enhance practicability, we derive adjustable parameters and formulate an optimization problem to minimize over-the-air aggregation errors. Furthermore, with an effective proof generation method, AirDIV can defend against two common attacks, i.e., replay and forge attacks. We provide a theoretical analysis of AirDIV’s correctness, accuracy and security, while conducting extensive experiments on both simulated and real platforms to validate its efficiency.

Abstracts:This work studies the challenge of scheduling real-time control commands in Networked Control Systems (NCS), where control actions rely on the freshness of data collected from multiple sources. In dynamic environments, ensuring that control commands in an NCS are accurate and frequent is essential for maintaining the system responsiveness. For this aim, we introduce a new metric, Age of Result (AoR), which quantifies the time elapsed since the last control command was generated and executed. This metric reflects the system’s capability to adapt to real-time changes in the operational environment by considering both data freshness and control command frequency. We conduct a detailed analysis of AoR in NCS, paying special attention to the dependencies between sensing and computing phases. We first address computation-intensive and network-intensive scenarios, proposing random sampling (RS)-based approximate algorithms for each case. Subsequently, we develop another RS-based algorithm and a heuristic approach for the general model. Simulation results demonstrate that our approach can effectively minimize AoR and significantly enhance the system performance and real-time adaptability compared to existing strategies.

Abstracts:The rapid advancement of 6G, cloud-fog computing, and internet of things (IoT) has revolutionized the control paradigm of smart grid. With the closed coupling between communication and power domains, control performance heavily relies on timely and secure sensing, transmission, and computing of grid state information. Conventional approaches which treat the four sectors as separate subsystems suffer from slow convergence and even cascading control oscillations. In this paper, we address the key research problem of sensing-transmission-computing-control integrated optimization to minimize the overall voltage deviation. A timeliness-driven integrated optimization algorithm is proposed, where proactive optimization of communication resource adaptation and power-domain control decisions is conducted based on the evolution of information timeliness loss in sensing, transmission, and computing, as well as its impact on control accuracy. Particularly, a self-penalty based cost function is developed to quantify the mismatch between communication-domain resource allocation and voltage control deviation. Moreover, a novel timeliness indicator, named age of trustworthy information (AoTI), is introduced to capture timeliness-trustworthiness performance loss on proportional-integral (PI) consensus control stability margin. Consensus weights are optimized based on AoTI to further enhance convergence speed and improve control accuracy. Simulation results demonstrate that the proposed algorithm significantly improves power-domain control stability, validating the efficiency of AoTI as a critical indicator for control information importance.

Abstracts:The timely exchange of information among robots within a team is vital, but it can be constrained by limited wireless capacity. The inability to deliver information promptly can result in estimation errors that impact collaborative efforts among robots. In this paper, we propose a new metric termed Loss of Information Utility (LoIU) to quantify the freshness and utility of information critical for cooperation. The metric enables robots to prioritize information transmissions within bandwidth constraints. We also propose the estimation of LoIU using belief distributions and accordingly optimize both transmission schedule and resource allocation strategy for device-to-device transmissions to minimize the time-average LoIU within a robot team. A semi-decentralized Multi-Agent Deep Deterministic Policy Gradient framework is developed, where each robot functions as an actor responsible for scheduling transmissions among its collaborators while a central critic periodically evaluates and refines the actors in response to mobility and interference. Simulations validate the effectiveness of our approach, demonstrating an enhancement of information freshness and utility by 98%, compared to alternative methods.

Abstracts:Along with the rapid development of the fourth industrial revolution, industrial cyber-physical systems (ICPS) are anticipated to achieve precise mapping and management for the physical world by integrating digital sensing and automated control. However, the conflict between limited computing resources and extensive sampling data, combined with severe industrial interference, exacerbates the system’s processing burden and diminishes its accuracy, hindering its ability to meet the low-latency and high-reliability control requirements. To address this issue, this paper investigates an end-edge collaborative control framework to enhance control performance for a short-packet transmission ICPS by providing powerful computation capability. We utilize the age of information (AoI) to characterize the impact of information freshness on control accuracy and construct an AoI-aware control law to assist in data sensing, transmission, and computing strategy design. In addition, we consider the influence of sampling and short-packet decoding errors in AoI-aware control performance to enhance the reliability of sampling and transmission strategies design. A joint optimization scheme of sampling interval, sampling time, computation offloading, and bandwidth allocation based on the block coordinate descent method and game theory is proposed to achieve a tradeoff between the control cost and energy consumption. By considering a real-world trolley inverted pendulum manipulation model, numerical results verify the performance gain of the proposed end-edge collaborative framework and the effectiveness of the presented algorithm.