Smart Industrial Internet of Things Devices, Services, and Applications: Ubiquitous Sensing and Sensory Data, Predictive Analytics Algorithms, and Cognitive Computing Technologies

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.

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.

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.

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.

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.

Industrial Internet of Things (IIoT) applications can greatly benefit from leveraging edge computing. For instance, applications relying on deep neural network (DNN) models can be sliced and distributed across IIoT devices and the network edge to reduce inference latency. However, low network performance between IIoT devices and the edge often becomes a bottleneck. In this study, we propose ScissionLite, a holistic framework designed to accelerate distributed DNN inference using lightweight data compression. Our compression method features a novel lightweight down/upsampling network tailored for performance-limited IIoT devices, which is inserted at the slicing point of a DNN model to reduce outbound network traffic without causing a significant drop in accuracy. In addition, we have developed a benchmarking tool to accurately identify the optimal slicing point of the DNN for the best inference latency. ScissionLite improves inference latency by up to 15.7× with minimal accuracy degradation.

Multiple studies show student dropout rates across rich, low and middle-income countries. With the development of machine learning, various learning institutions have initiated predictive analytics algorithms to solve this issue. However, there is limited knowledge on how this strategy has been employed to optimize retention rates. The research aims to comprehensively review PAAs Strategies and demonstrate how they have been effectively implemented in optimizing retention rates with a case study focusing on North America. The study employed a quantitative research methodology and thematic study design to review the existing literature. The result shows that learning institutions used a combination of data mining classifiers such as k-Nearest Neighbor, Neural Networks, Decision Tree, and Naive Bayes to categorize student dropout rates and optimize retention rates. The PAAs approach has been utilized by various educational institutions across North America, applying multiple predicting algorithms to ensure efficiency. Keywords: Predictive analytics algorithms, mitigating dropout rates, retention rates, classifiers, Learning institutions, North America

Data created at industrial sites through industrial internet of things devices are now being processed automatically or in real-time in the industrial structure, due to the application of artificial intelligence technology to industrial sites. However, the expenses of autonomous or real-time data processing and steady data processing (analysis, prediction, prescription, and implementation) necessitate a new processing method. We propose a blockchain-based industrial internet of things information reinforcement model in this work that may reliably ensure the integrity of industrial internet of things data produced at industrial locations. The proposed model processes industrial internet of things data that may occur at endpoints at industrial sites into the blockchain by processing data generated by the same industrial internet of things device independently. As a result, the IIoT data sent to the industrial internet of things server can be evaluated more readily, and production accuracy may be enhanced. The proposed model optimizes industrial internet of things information linkage by stochastically reflecting the information based on attribute value frequency. By dynamically aggregating the related data of industrial internet of things information acquired as a seed through hierarchical subnets, the proposed model increases stability and accuracy. Furthermore, the proposed model may be used to enhance an organizations’ operational efficiency (consulting and training, for example) and strategic decision-making by utilizing fundamental knowledge about items produced at industrial locations. Furthermore, the proposed model allows for information sharing and system connectivity between industrial locations, allowing for close collaboration between industrial internet of things features. As a result of the performance evaluation, the proposed model included an industrial internet of things sensor to the blockchain, eliminating the need for an extra function in the manufacturing process and reducing the time required to validate the integrity of industrial internet of things data. In addition, as a result of analyzing industrial internet of things data by an algorithm according to the number of simulated clouds, the accuracy of industrial internet of things information was improved by 2.5% to 3%, on average.

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.

Cyber–physical system (CPS) provides a foundation for the Industrial Internet of Things (IIoT) that interconnects all types of devices. The integration of CPS with IIoT generates the large volumes of data forcing the development of artificial intelligence (AI) to extract information more precisely. Nevertheless, the increasing volume/variety of data traffic and the ever-growing number of IIoT devices bring great challenges for the host-centric communication model of the current Internet. In this work, we present a novel information-centric networking (ICN)-based system model in CPS, which enables processing data from IIoT devices closer to the edge as opposed to a content provider. Based on this ICN system model, we propose an edge-assisted authentication scheme in CPS, aiming to protect the system from unauthorized access and reduce workload for resource-constrained devices. The main features of our scheme include a delegation model of security operations and session handshake procedures through edge routers, addressing the rising challenges in managing and securing IIoT devices in the ICN. We formally prove the security of our scheme and conduct performance analysis to show its practicality.

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%.

This article explains the problems caused by the increasing density and variety of information in the modern business world and the integration of social studies, which has always been opposed to statistical methods because of its developments dating back to the Internet era. To solve this problem, scientists and researchers have developed the best analytical methods that can provide better business insight. This chapter focuses on the main features that underpin social big data analytics (SBDA) and provides a framework for predictive modeling in this context. Predictive analytics is of particular interest as an important part of SBD because it enables businesses to make informed decisions and forecasts based on data models and trends. The SBD estimation process will include a variety of methods, tools and techniques that help make accurate predictions and extract observed points from various social data. Additionally, this section discusses various predictive analytics algorithms, their applications in core applications, and the use of high-level tools and APIs to facilitate data analysis in society. These algorithms and tools help companies extract valuable insights from big data communities to make better decisions and strategic plans. Experimental and research data will be presented in this section to reinforce the importance and value of estimation in the context of SBD. These experiments demonstrate real-world applications and show how predictive analytics can be used effectively to gain insights from data relationships and drive meaningful business results. Overall, this chapter focuses on clarifying the importance of predictive analytics in dealing with big data on social media, demonstrating its benefits, and how volunteering can be used to solve world problems and improve the economy.

This article considers a physical layer-based security strategy to achieve specifics cyber security target levels for resource-constrained industrial Internet of Things (IoT) devices affected by interference attacks. To achieve target information availability measures during adversarial interference, a cooperative communication scheme between select industrial IoT devices is proposed in order to limit signal outages and to reduce the impact of interference attacks on the industrial IoT devices. The analytical and numerical analysis in this article demonstrate the ability of the proposed cooperative transmission strategy to achieve target information availability levels during interference attacks on industrial IoT devices.

Massive multiple-input–multiple-output (MIMO) systems which include a large number of service antennas have received a great deal of attention as a means to improve both spectral efficiency (SE) and energy efficiency (EE) of wireless communication systems. In this article, we study the feasibility of using Massive MIMO to support a vast amount of underwater Industrial Internet of Things (IIoT) devices. By using a large number of antennas as compensation entities for the low bandwidth in the underwater channel, we show that underwater Massive MIMO can simultaneously support a massive amount of underwater IIoT devices. Electromagnetic radio frequency (RF) and acoustic waves are used to present the performance with respect to SE, throughput (TP) per user equipment (UE), and coverage. Additionally, we show that both Massive MIMO with RF and acoustic waves have the same closed-form upper bounds which can be used as an important design tool of underwater IIoT networks. Numerical results verify the theoretical analysis.