Federated Reinforcement Learning for IIoT: A Multi-Agent Approach to QoS Optimization

In this work, we design an elastic open radio access network (O-RAN) slicing for the Industrial Internet of things (IIoT). Due to the rapid spread of IoT in the industrial use-cases such as safety and mobile robot communications, the IIoT landscape has been shifted from static manufacturing processes towards dynamic manufacturing workflows (e.g., Modular Production System). But unlike IoT, IIoT poses additional challenges such as severe communication environment, network-slice resource demand variations, and on-time information update from the IIoT devices during industrial production. First, we formulate the O-RAN slicing problem for on-time industrial monitoring and control where the objective is to minimize the cost of fresh information updates (i.e., age of information (AoI)) from the IIoT devices (i.e., sensors) with the device energy consumption and O-RAN slice isolation constraints. Second, we propose the intelligent O-RAN framework based on game theory and machine learning to mitigate the problem’s complexity. We propose a two-sided distributed matching game in the O-RAN control layer that captures the IIoT channel characteristics and the IIoT service priorities to create IIoT device and small cell base station (SBS) preference lists. We then employ an actor-critic model with a deep deterministic policy gradient (DDPG) in the O-RAN service management layer to solve the resource allocation problem for optimizing the network slice configuration policy under time-varying slicing demand. Furthermore, the proposed matching game within the actor-critic model training helps to enforce the long-term policy-based guidance for resource allocation that reflects the trends of all IIoT devices and SBSs satisfactions with the assignment. Finally, the simulation results show that the proposed solution enhances the performance gain for the IIoT services by serving an average of 50% and 43.64% more IIoT devices than the baseline approaches.

This position article presents dispersed automation, a novel approach for reprogramming the omnipresent industrial Internet of Things (IIoT) devices in critical infrastructure for the co-execution of general-purpose computation workloads in an optimal and reliable manner. The key observation is that IIoT devices are available in large quantities in critical infrastructure. However, these devices are often underutilized due to the conservative allocation of resources, static task assignment, and lack of exploitation of parallelism. Dispersed automation gives new life to IIoT devices, some of them as powerful as general-purpose computers, and makes them accessible for the co-execution of various computational workloads, while dynamically adapting to different applications and operational environments. Two key contributions make this possible: the use of a domain-specific language and on-channel computation. We present dispersed automation as an enabling technology for the implementation of future smart and adaptive IIoT systems. Note to Practitioners —In this article, we explain our concept and vision on how programs can be created and designed in a way that makes it possible to run them on IIoT networks with a greater amount of flexibility than we have today. With this technology, it will be possible for programs to adapt to changes in their operation, and to changes in the network environment, in order to better utilize the available hardware resources. This means that a dispersed automation system can continue its operation in certain situations where a traditionally deployed system would fail due to insufficient resources (e.g., memory, processing speed). It also means that the software can make better use of the underlying hardware whenever a special task needs to be carried out (e.g., a “heavy” diagnostic process). We show some experimental results of using our approach based on simulation. The way forward is to implement and evaluate this technology in an industrial setting.

This paper provides an overview of several secure boot architectures with a focus on key rotation. It expands on a practitioner note that the authors submitted to the 2023 IEEE Secure Development Conference. Key rotation is important due to the frequency of lost signing keys and the difficulty of managing secret keys for the long lifetimes of Industrial Internet of Things (IIOT) devices. Key rotation is not simple for IIOT due to limited resources during a secure boot process and the constraints of the firmware utilities that come from the chip vendors. This paper reviews and compares five common architectures for a secure boot that are seen across the IIOT community. For each architecture, it provides some key strengths and weaknesses associated with that architecture. The paper then provides a detailed comparison and analysis of the architectures to convince the IIOT community to move towards a strong use of certificates (instead of the traditional use of raw public keys). The intent of this paper is to provide a practitioner’s perspective on these challenges and the tradeoffs in hopes of inviting comments from chip vendors and the broader community.

Our planet has a tremendous problem with the air pollution and climate change. The last study published by Jos Lelieveld et al. [1] remarks the high exposition risk of EU Citizens to suffer cardiovascular diseases due to our poisonous air quality, killing yearly 800.000 EU citizens and reducing 2.2 years our life expectancy. Additionally, the last report presented on the UN conference for climate change on 25th of September'19, the IPPC, sets that if the water temperature increases 2°C, the sea level will rise 42cm from this year to 2100 but. If it increases 3°C, the rise will be 84 cm. Besides, it has been demonstrated that the carbon footprint associated with various human activities leads to a steady increase in global mean temperature. Most of the gases that human activity emits into the atmosphere are due to the industrial processes that require a lot of energy for the transformation of the raw materials. Furthermore, a large part of the energy consumed in the industry is dissipated in the form of heat, also called waste heat. As a clear example, in the EU27, it is estimated that more than 65% of the energy used in the energy-intensive industries (EII) industry is lost in form of waste heat, representing yearly the 21% the EU energy needs. This paper presents new wireless and battery-less industrial Internet of Things (IIoT) devices powered by waste heat for measuring vibrations in rotative machines, called INDUEYE IIoT. These self-powered devices will help huge energy demanding industries (especially chemical, petrochemical, oil refineries, etc.) to become more environmentally friendly and profitable in their digitalization process towards Industry 4.0. Also, these new industrial sensing devices take benefit of the new long-range wireless protocols such as NB-IoT or LoRa that simply eliminate all the wireless infrastructure in the facility (and its associated costs). Additionally, the edge-computing concept is introduced in the INDUEYE IIoT device decreasing to 98% of the cloud computation, reducing in consequence, the cloud computing carbon footprint, and its associated fees.

Along with the fourth industrial revolution, smart factories are receiving a great deal of attention. Large volumes of real-time data that are generated at high rates, especially in industries, are becoming increasingly important. Accordingly, the Industrial Internet of Things (IIoT), which connects, controls, and communicates with heterogeneous devices, is important to industrial sites and is now indispensable. To ensure the fairness and quality of the IIoT with limited network resources, the network connection of the IIoT needs to be constructed more intelligently. Many studies are being conducted on the efficient use of the resources that are imposed on IIoT devices. Therefore, in this paper, we propose a collaboration optimization method for heterogeneous devices that is based on cloud–fog–edge architecture. First, this paper proposes a knowledge distillation-based algorithm that can collaborate on cloud–fog–edge computing on the basis of distributed control. Second, to compensate for the shortcomings of knowledge distillation, we propose a framework for combining a soft-label-based alarm level. Finally, the method that is proposed in this paper was verified through several experiments, and it is shown that this method can effectively shorten the response time and solve the problems of existing IIoT networks, and that it can be efficiently applied to heterogeneous devices.

Blockchain and federated learning-based intrusion detection approaches for edge-enabled industrial IoT networks: a survey

Industrial Internet of Things (IIoT) ensures reliable and efficient data exchanges among the industrial processes using Artificial Intelligence (AI) within the cyber-physical systems. In the IIoT ecosystem, devices of industrial applications communicate with each other with little human intervention. They need to act intelligently to safeguard the data confidentiality and devices' authenticity. The ability to gather, process, and store real-time data depends on the quality of data, network connectivity, and processing capabilities of these devices. Pervasive Edge Computing (PEC) is gaining popularity nowadays due to the resource limitations imposed on the sensor-embedded IIoT devices. PEC processes the gathered data at the network edge to reduce the response time for these devices. However, PEC faces numerous research challenges in terms of secured communication, network connectivity, and resource utilization of the edge servers. To address these challenges, we propose a secured and intelligent communication scheme for PEC in an IIoT-enabled infrastructure. In the proposed scheme, forged identities of adversaries, i.e., Sybil devices, are detected by IIoT devices and shared with edge servers to prevent upstream transmission of their malicious data. Upon Sybil attack detection, each edge server executes a parallel Artificial Bee Colony (pABC) algorithm to perform optimal network configuration of IIoT devices. Each edge server performs the job migration to their neighboring servers for load balancing and better network performance, based on their processing and storage capabilities. The experimental results justify the efficiency of our proposed scheme in terms of Sybil attack detection, the convergence curves of our pABC algorithm, delay, throughput, and control overhead of data communication using PEC for IIoT.

The Industrial Internet of Things (IIoT) integrated with cloud computing resources offers effective advancements in the field of industrial automation with open connectivity and emergent computing. With such advancements, the supplementary services evolves in order to augment the automation process in manufacturing and production industries. However, the integration and transformation of cloud and IIoT environment poses serious limitation on reliability and computational performance. It is very necessary to develop a framework that handles and provides a bridge between the rapid input acquisitions from an IIoT device and rapid data processing or storage by the cloud. In this paper, model based engineering (MBE) model is used to control the workflow in cloud environment, which is integrated with a deep learning algorithm named recurrent neural network (RNN) algorithm. The RNN is utilized to control the future workload prediction and supply necessary resources to balance the workflow in cloud computing environment. The entire predictions are carried out based on the input data acquisition rate from the IIoT devices. The storage or processing of the input data is carried out by the Virtual Machines presents in cloud environment. The resources allocation and scheduling of workload is carried out in the control plane using RNN that optimizes the workflow. The RNN is resumed to solve iteratively the task of optimization. The experiments are conducted to test the efficacy of the model and the results are verified in terms of total energy consumption, task response time, and VM Utilization. The experiments are conducted with same/different types of IIoT devices. The simulation results reveal the improved efficacy of the MBE–RNN model than existing deep learning and MBE schedulers. The results show that the MBE–RNN acquires improved processing ability with reduced task response time and energy utilization.

Industry 4.0 is the next revolution in manufacturing technology that is going to change the production and distribution of goods and services within the following decade. Powered by different enabling technologies that are also being developed simultaneously, it has the potential to create radical changes in our societies such as by giving rise to highly‐integrated smart cities. The Industrial Internet of Things (IIoT) is one of the main areas of development for Industry 4.0. These IIoT devices are used in mission‐critical sectors such as the manufacturing industry, power generation, and healthcare management. However, smart factories and cities can only function when threats to cyber security, data privacy, and information integrity are properly managed. In this regard, securing IIoT devices and their networks is vital to preserving data and privacy. The use of artificial intelligence is an enabler for more secure IIoT systems. In this study, we propose high‐performing deep learning models for the classification of botnet attacks that commonly affect IIoT devices and networks. Evaluation of results shows that deep learning models such as the artificial neural network (ANN), the long short‐term memory (LSTM), and the gated recurrent unit (GRU) can successfully be used for classifications of IIoT malware attacks with an accuracy of up to 99%.

The sixth generation (6G) is conceived to address the expected high level of requirements (such as ultra-high-data-transmission rate, support for the highest moving speed and seamless connection, etc.) in the next decade and beyond. In the context of 6G, a large number of Industrial Internet of Things (IoT) (IIoT) devices may access the network, and thanks to the rapid development of artificial intelligence make smart manufacturing has the opportunity to be realized. However, a large number of IoT devices, the tremendous volume of data, the heterogeneous nature of devices, and the increasing concerns of privacy challenge the efficient management and quality of services in IIoT. To address these problems, in this article, a device-to-device (D2D) communication-aided digital twin edge network is proposed, where edge computing is introduced to bring computing and storage resources near to the end devices, and digital twin is utilized to fill the gap between physical and virtual space and D2D communication is applied to assist resource limited IoT devices to achieve normal communication. Moreover, digital twin-empowered federated reinforcement learning is leveraged to provide privacy awareness and decentralized resource allocation strategy training on D2D communication links to further improve network performance. The simulation results show that the proposal achieves significant network performance compared with baseline algorithms.

Complex-valued hyperchaos-assisted vector-valued artificial neural key coordination for improving security in the Industrial Internet of Things

The Industrial Internet of Things (IIoT) technology has led to tremendous advancements in the field of real-time data collection, automation and intelligent decision-making. However, with an increased number of IIoT devices, security and privacy concerns, including unauthorized access, data manipulation and limited traceability, have emphasized the importance of addressing the risk. Among all the tools used, blockchain technology has gained prominence as a tool to reduce problems like data manipulation and unauthorized access. The authors of this study have developed a comprehensive review of the existing research, its application and the framework and integration of blockchain technology with IIoT for securing IIoT and related devices. Moreover, the study has also revealed key limitations of applying this technology, including transaction delay, resource consumption and scalability. Finally, this study has included an effective and comparative analysis with other technologies and solutions, providing readers with evidence-based and authentic guidelines on the viability of this technology. Moreover, the study has emphasized certain key factors of this technology and its application with IIoT, including enhanced trust, traceability and security, with consideration of the discussed key limitations. Based on the study, it can be clearly deduced and asserted that blockchain technology can play an effective and critical role in developing a novel and robust security mechanism for IIoT, including other related devices.

Ensuring safety in the mining industry is a critical concern for a nation's industrial advancement. Industry 4.0, characterized by the integration of advanced technologies, is at the forefront of efforts to enhance mining practices. Coal seams contain a range of hydrocarbon gases, predominantly methane, which is released in significant quantities during mining operations. Effectively mitigating methane emissions is imperative. The inclusion of methane forecasting allows for the early identification of potential methane emissions, hence resulting in significance enhancement in mine safety. The research work is focused on real-time remote monitoring and cloud-based forecasting of methane levels in underground coal mines. An Industrial Internet of Things (IIoT) device is developed for data acquisition in underground coal mines, capturing essential parameters such as methane concentration, temperature, and humidity. The collected data are utilized to train a long short-term memory based multivariate forecasting model. The trained model is subsequently deployed in the cloud. The experiment is performed in a mine of Eastern Coalfields Limited, India. After the deployment of the proposed model, the developed IIoT device transmits real-time data, obtained from the mine, to the cloud. Based on the real-time data, our model conducts methane forecasting and communicates results back to the IIoT device. The device issues immediate alerts when methane levels surpass predefined thresholds. This ensures enhanced safety in mining operations by providing warnings for both current and forecasted methane concentrations. The forecasted methane concentrations, along with real-time data, are accessible through mobile applications and a web-based dashboard. The accuracy of the proposed model is measured by mean absolute error, mean absolute percentage error, and root mean square error, which demonstrate values of 156.95 ppm, 4.23%, and 191.53 ppm, respectively. A comparative study is performed where our model is evaluated against the multivariate multilayer perceptron, vector autoregression, and auto-regressive integrated moving average models. The comparative study demonstrates that our developed model outperforms the others, showing superior results.

In real-world Industrial Internet of Things (IIoT) scenarios, due to the limited storage capacity of IIoT devices, fresh data continuously received by diverse devices will overwrite the outdated data and change the local data distribution. However, state-of-the-art studies have demonstrated that federated learning tends to focus on training with fresh data, and the latest global model may forget the historical update directions (i.e., catastrophic forgetting). This issue can significantly degrade the global model accuracy. Existing methods primarily focus on integrating outdated data characteristics into fresh data but overlook the large parameter update gap between global and local models during global aggregation. This gap can cause the global model updates to deviate from the optimal direction. To this end, we propose a federated adaptive weighted aggregation method based on model consistency (FedAWAC). Specifically, FedAWAC measures the model consistency on devices and dynamically adjusts the aggregation weights of each local model, thereby guiding the global model toward optimal updates. Furthermore, FedAWAC integrates <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mathcal {M}$ </tex-math></inline-formula> historical global models most correlated to the latest global model on the cloud server to overcome catastrophic forgetting. Experiments on four different datasets (nonidentically and independently distributed settings) indicate that compared to five baselines, FedAWAC can improve global model accuracy by an average of 1.86%, reduce the forgetting rate by an average of 3.91%, and save average memory usage by up to 2.57 GB.

The extensive propagation of industrial Internet of Things (IIoT) technologies has encouraged intruders to initiate a variety of attacks that need to be identified to maintain the security of end-user data and the safety of services offered by service providers. Deep learning (DL), especially recurrent approaches, has been applied successfully to the analysis of IIoT forensics but their key challenge of recurrent DL models is that they struggle with long traffic sequences and cannot be parallelized. Multihead attention (MHA) tried to address this shortfall but failed to capture the local representation of IIoT traffic sequences. In this article, we propose a forensics-based DL model (called Deep-IFS) to identify intrusions in IIoT traffic. The model learns local representations using local gated recurrent unit (LocalGRU), and introduces an MHA layer to capture and learn global representation (i.e., long-range dependencies). A residual connection between layers is designed to prevent information loss. Another challenge facing the current IIoT forensics frameworks is their limited scalability, limiting performance in handling Big IIoT traffic data produced by IIoT devices. This challenge is addressed by deploying and training the proposed Deep-IFS in a fog computing environment. The intrusion identification becomes scalable by distributing the computation and the IIoT traffic data across worker fog nodes for training the model. The master fog node is responsible for sharing training parameters and aggregating worker node output. The aggregated classification output is subsequently passed to the cloud platform for mitigating attacks. Empirical results on the Bot-IIoT dataset demonstrate that the developed distributed Deep-IFS can effectively handle Big IIoT traffic data compared with the present centralized DL-based forensics techniques. Further, the results validate the robustness of the proposed Deep-IFS across various evaluation measures.