In this paper, we propose a novel channel-aware latency taming scheme, called Optimal Transmission Latency Taming (OTLT), to detect hidden channel state and tame the distribution tail of the packet sojourn time in Industrial Internet of Things (IIoT) devices. Specifically, we design a forward algorithm based on a hidden semi-Markov model to detect the hidden channel state, with a particular emphasis on the state sojourn duration, and to calculate the corresponding channel access probability. Then we develop a time-sensitive model to investigate the minimum sojourn time a packet spends in the IIoT device before leaving successfully. With the obtained channel access probability, the first passage probability of the proposed model is explored to find the maximum probability of a packet being successfully transmitted in a given back-off sojourn duration (BSD). The distribution tail of the packet sojourn time can be tamed by minimizing the cumulative summation of each BSD in consideration of the quadratic penalty latency constraints. Simulation results demonstrate that, in the industrial environment, the OTLT scheme can keep the packet’s sojourn duration within a quantifiable limit and variance. It can also obtain considerably efficient control over packet transmission latency in a time-varying wireless propagation channel even with the increasing number of IIoT devices.

Recent developments in industrial wireless sensor networks (IWSNs) have revolutionized industrial automation systems. However, harsh industrial environment poses great challenges to a time-critical and reliable wireless communication. For instance, effects of multipath fading, noise and co-channel interference can have unpredictable and time-varying impacts on the propagation channel, leading to the failure of on-time packet delivery. To address this problem, in this paper we propose a channel-based Optimal Back-off Delay Control (OBDC) scheme which can minimize the total time a packet spends in the sensor node (TSN) by assessing the features of a generic wireless channel. Specifically, we first explore the channel impairments by investigating the probability density function (PDF) of the level crossing rate (LCR) of the received signal in the industrial wireless environment. Then, with the obtained channel assessment results, we develop a phase-type semi-Markov model to investigate the probability distribution of the back-off delay of a packet in the sensor node (SN). The probability distribution of the back-off delay can be further substituted with TSN according to the queuing theory. The proposed OBDC scheme examines the Kullback-Leibler (KL) divergence between the obtained distribution of TSN and the packet arrival rate, and reduces the TSN according to an objective function which is constantly renewed in every transmission round with regard to a delay constraint. The simulation results show that the OBDC scheme can reduce TSN and guarantee to keep the TSN in an acceptable range even though the wireless channel is impaired by interference effects. It also shows that the OBDC scheme can reduce the proportion of packets meeting their deadline to the total packets in transmission when the number of SN and LCR changes

Building digital twins (DTs) in industrial Internetof- Things (IIoT) is challenging, especially considering complex and large-scale network architectures, real-time data requirements, and computational demands. Traditional modeling-based approaches, relying on either small datasets with physics-based models or large datasets processed through artificial intelligence (AI) techniques, face limitations in adaptability and accuracy. In this article, we propose a hybrid DT framework to address these challenges, which integrates both physics-based models and AI techniques. The physics-based models, grounded in communication, computing, and caching (3C) resources, ensure alignment with known system behaviors and predetermined assumptions, while the AI components dynamically adapt to real-time network environments, allowing the DT to learn from the evolving environments. The proposed DT framework incorporates layers that support real-time data acquisition, data processing, and decision-making through continuous feedback, enhancing system performance and enabling proactive maintenance, quality control, and optimization. A hybrid model-based case study demonstrates that the proposed framework can reduce packet queuing size and improve network performance under varying network load and outage conditions. Finally, open research issues for DT in IIoT are discussed.

Empowered by the large amounts of sensor data in the Industrial Internet of Things, data-driven fault diagnosis has a pivotal role in improving equipment reliability in harsh industrial environments. To enhance diagnostic performance under unknown operating conditions, transfer learning-based cross-domain fault diagnosis has been emerging. However, diagnostic models are prone to overfit to the source domain due to the lack of sample diversity when only a single source domain is available. Moreover, significant domain shifts between the single source domain and multiple unknown target domains may degrade the generalization performance on the unknown domains. To address these challenges, we propose a Multi-pseudo Domains Augmented Adversarial Domain-invariant learning (MDA-AD) for cross-domain fault diagnosis. First, we design a multi-pseudo domain generator, where inter-domain diversity constraints and manifold-semantic consistency constraints are implemented to avoid overfitting on the source domain by generating diverse and representative pseudo samples. Subsequently, to alleviate the domain shift, we design an adversarial domain-aware classifier that extracts domain-invariant features by introducing an adversarial paradigm between a feature extractor and a domain discriminator. Finally, to further enhance the diversity of the pseudo domains, we implement a diversity-consistency constrained domain-invariant training strategy. The experimental results, obtained through comparative studies, hyperparameter influence analysis, and visualization on two bearing datasets, affirm the superior diagnostic performance of MDA-AD in a single source domain.

The widely deployment of infrastructure and wireless interfaces increases industrial IoT (IIoT) vulnerability to network intrusions, highlighting the requirements for robust network intrusion detection systems (NIDSs). Although deep learning (DL) provides a promising solution for NIDSs, it remains susceptible to adversarial attacks as minor input perturbations can lead to major misclassifications. In this paper, we propose a packet-level adversarial traffic generation (PATG) approach for attacking NIDSs in IIoT, which not only aligns with domain constraints but also evades various DL-based NIDSs. Particularly, we introduce a reversible abstract traffic representation to ensure that the original traffic can be effectively modified while preserving its functionality. We propose a packet-level generative adversarial networks to craft adversarial traffic by learning benign data distribution in feature space and simulating evasion behaviors, which escapes the DL-based NIDSs. We further design two defense schemes to enhance system resilience against proposed adversarial attacks. We evaluate PATG on nine state-of-the-art DL-based NIDSs in the Kitsune and CICIoT23 datasets. Experimental results demonstrate that PATG can achieve a maximum evasion increase rate of 99% with cost-effective execution, while the defense methods significantly mitigate the impact of the adversarial attacks.

In this work, we investigate the age of information (AoI) in aerial intelligent reflecting surfaces (IRS) assisted covert data collection in Internet of Things (IoT) networks. Operating with the unmanned aerial vehicle (UAV) and IRS can improve the data transmission covertness, as well as the data freshness by reconstructing the wireless propagation environment. Specifically, we consider a scenario in which ground IoT sensors transmit confidential information to the legitimate receiver (Bob), through IRS assisted UAV relay, at the same time, an eavesdropper passively listens to and intercepts the confidential information sent by sensors. To minimize the average AoI of the system, a joint UAV trajectory and IRS phase shift optimization problem is formulated under the constraints of covertness requirement. The minimum error detection probability and optimal detection threshold are first derived at Willie, which represents the worst-case situation for the legitimate transmission. The constructed problem is a mixed integer programming NP-Hard problem, which is more complex using traditional convex optimization methods. Therefore, the online learning, i.e.,deep reinforcement learning is leveraged to obtain the near-optimal solution. Details, the Deep Q Network (DQN) and Deep Deterministic Policy Gradient (DDPG) methods are utilized. Numerical results show that deep reinforcement learning can improve the information freshness of the system by reasonably designing UAV trajectory and IRS phase shift under a given covertness constraint.