Industrial Internet Of Things Solutions Research Articles

A Dynamic IIoT Framework Based on the Publish-Subscribe Paradigm.

The use of the Internet of Things (IoT) technologies and principles in industrial environments is known as the Industrial Internet of Things (IIoT). The IIoT concept aims to integrate various industrial devices, sensors, and actuators for collection, storage, monitoring, and process automation. Due to the complexity of IIoT environments, there is no one-size-fits-all solution. The main challenges in developing an IIoT solution are represented by the diversity of sensors and devices, connectivity, edge/fog computing, and security. This paper proposes a distributed and customized IioT (Industrial Internet of Things) framework for the interaction of things from the industrial environment. This framework is distributed on the fog nodes of the IIoT architecture proposed, and it will have the possibility to interconnect local things (with low latency) or global things (with a latency generated by the Internet network). To demonstrate the functionality of the proposed framework, it is included in the fog nodes presented in other paper. These fog nodes allow the integration of CANOpen networks into an IioT architecture. The most important advantages of the proposed architecture are its customizability and the fact that it allows decision operations to be carried out at the edge of the network to eliminate latency due to the Internet.

IOT MEETS THE WEB: INNOVATIVE APPROACHES IN WEB-ENABLED IOT SOLUTIONS

The rapid convergence of the Internet of Things (IoT) and web development is ushering in a new era of technological innovation. This paper explores the synergistic relationship between IoT and web technologies, highlighting innovative approaches and solutions in IoT-web integration. Key areas of focus include real-time data handling, enhanced user experience design, scalable architectures, and advanced security measures. The paper also delves into emerging technologies such as AI, ML, blockchain, and edge computing, examining their role in enhancing IoT web applications. Through case studies in smart home systems, healthcare monitoring, and industrial IoT solutions, the paper illustrates practical applications and the transformative impact of these integrations. The paper concludes with a discussion of future trends, potential innovations, and the challenges and opportunities ahead, providing insights into the evolving landscape of IoT and web development..

Virtualization Over Multiprocessor Systems-on-Chip: An Enabling Paradigm for the Industrial Internet of Things

The next-generation Industrial Internet of Things (IIoT) inherently requires smart devices featuring rich connectivity, local intelligence, and autonomous behavior. Emerging Multiprocessor System-on-Chip (MPSoC) platforms along with comprehensive support for virtualization will represent two key building blocks for smart devices in future IIoT edge infrastructures. We review representative existing solutions, highlighting the aspects that are most relevant for integration in IIoT solutions. From the analysis, we derive a reference architecture for a general virtualization-ready edge IIoT node. We then analyze the implications and benefits for a concrete use case scenario and identify the crucial research challenges to be faced to bridge the gap towards full support for virtualization-ready IIoT nodes

Experimental evaluation of multi-PHY 6TiSCH networks

The architectural design of Wireless Sensor Networks (WSNs) for the Industrial Internet of Things (IIoT) applications requires the careful planning and selection of an appropriate operational strategy. Harmonization of standards is crucial to ensure easier certification and commercialization of IIoT solutions. The ongoing research activities are directed toward designing agile, reliable, and secure transmission technologies and protocols. Recently, Time Slotted Channel Hopping (TSCH) standardization bodies have started to consider support for multiple physical layers thus accommodating a wide range of applications. This paper presents the results of the extensive experimental measurement campaign to study the performance of the 6TiSCH (IPv6 over the TSCH mode of IEEE 802.15.4e) network while supporting multiple physical layers (PHYs). For measurement purposes, all experiments were performed on OpenMote-B hardware. These devices are equipped with an Atmel AT86RF215 dual radio transceiver implementing IEEE 802.15.4g. The performance evaluation is provided for the following metrics: network formation time, Packet Delivery Ratio (PDR), latency, and duty cycle. Results are encouraging, particularly in terms of high PDR for all tested PHYs. Performance evaluation indicates the importance of proper node positioning, link quality estimation and careful selection of network parameters. Moreover, collected experimental results create a dataset that provides insights into the tested PHYs' performance and their potential for indoor 6TiSCH networking.

Secure Blockchain Middleware for Decentralized IIoT towards Industry 5.0: A Review of Architecture, Enablers, Challenges, and Directions

Resilient manufacturing is a vision in the Industry 5.0 blueprint for satisfying sustainable development goals under pandemics or the rising individualized product needs. A resilient manufacturing strategy based on the Industrial Internet of Things (IIoT) networks plays an essential role in facilitating production and supply chain recovery. IIoT contains confidential data and private information, and many security issues arise through vulnerabilities in the infrastructure. The traditional centralized IIoT framework is not only of high cost for system configuration but also vulnerable to cyber-attacks and single-point failure, which is not suitable for achieving the resilient manufacturing vision in Industry 5.0. Recently, researchers are seeking a secure solution of middleware based on blockchain technology integration for decentralized IIoT, which can effectively protect the consistency, integrity, and availability of IIoT data by utilizing the auditing and tamper-proof features of the blockchain. This paper presented a review of secure blockchain middleware for decentralized IIoT towards Industry 5.0. Firstly, the security issues of conventional IIoT solutions and the advantages of blockchain middleware are analyzed. Secondly, an architecture of secure blockchain middleware for decentralized IIoT is proposed. Finally, enabling technologies, challenges, and future directions are reviewed. The innovation of this paper is to study and discuss the distributed blockchain middleware, investigating its ability to eliminate the risk of a single point of failure via a distributed feature in the context of resilient manufacturing in Industry 5.0 and to solve the security issues from traditional centralized IIoT. Also, the four-layer architecture of blockchain middleware presented based on the IIoT application framework is a novel aspect of this review. It is expected that the paper lays a solid foundation for making IIoT blockchain middleware a new venue for Industry 5.0 research.

IoT System for Detecting the Condition of Rotating Machines Based on Acoustic Signals

Modern predictive maintenance techniques have been significantly improved with the development of Industrial Internet of Things solutions which have enabled easier collection and analysis of various data. Artificial intelligence-based algorithms in combination with modular interconnected architecture of sensors, devices and servers, have resulted in the development of intelligent maintenance systems which outperform most traditional machine maintenance approaches. In this paper, a novel acoustic-based IoT system for condition detection of rotating machines is proposed. The IoT device designed for this purpose is mobile and inexpensive and the algorithm developed for condition detection consists of a combination of discrete wavelet transform and neural networks, while a genetic algorithm is used to tune the necessary hyperparameters. The performance of this system has been tested in a real industrial setting, on different rotating machines, in an environment with strong acoustic pollution. The results show high accuracy of the algorithm, with an average F1 score of around 0.99 with tuned hyperparameters.

Open Source IIoT Solution for Gas Waste Monitoring in Smart Factory.

Rapid development of smart manufacturing techniques in recent years is influencing production facilities. Factories must both keep up with smart technologies as well as upskill their workforce to remain competitive. One of the recent concerns is how businesses can contribute to environmental sustainability and how to reduce operating costs. In this article authors present a method of measuring gas waste using Industrial Internet of Things (IIoT) sensors and open-source solutions utilised on a brownfield production asset. The article provides a result of an applied research initiative in a live manufacturing facility. The design followed the Reference Architectural Model for Industry 4.0 (RAMI 4.0) model to provide a coherent smart factory system. The presented solution’s goal is to provide factory supervisors with information about gas waste which is generated during the production process. To achieve this an operational technology (OT) network was installed and Key Performance Indicators (KPIs) dashboards were designed. Based on the information provided by the system, the business can be more aware of the production environment and can improve its efficiency.

End-to-End Industrial IoT: Software Optimization and Acceleration

Application of artificial intelligence and machine learning is transforming Industrial Internet of Things (IIoT) segments by enabling higher productivity, better insights, less downtime, and superior product quality. Through AI-inspired innovations, businesses are gaining a sizable competitive edge and product leadership. To realize the promise and potential of AI, software needs to enable fast prototyping and experimentation at scale, and efficiently turn data into valuable insights. Over 100× performance speedup is possible with software that is well parallelized, vectorized, written with better data reuse, has cache blocking, makes good use of prefetching, and above all, is abstracted with familiar industry-standard APIs and libraries used by ML developers and data scientists. As a result of increased software performance efficiency, on the same hardware system, a customer can realize and serve higher insights per second and increase inference throughput. Our software optimizations target a general-purpose CPU, which, in addition to AI models and pipelines, can also be efficient through workload consolidation for other IIoT applications. Both general-purpose workloads and AI model inferencing run on the same hardware targets while minimizing energy, maintenance, and total cost of ownership of a heterogenous infrastructure. Edge IIoT also has unique power and space constraints that can be addressed well with multi-stream inferencing support on general-purpose CPUs. It is imperative to optimize software for all phases of the end-to-end AI pipeline to run “efficient AI” and realize quicker insights, thereby turning them into concrete business results for IIoT solutions. In this article, using optimized frameworks such as Intel Pytorch extensions, Intel Scikit-learn extensions, and Intel distribution of Modin, we get 3.6x-81 × improvement in end-to-end pipeline performance. What this means for the CNN-based anomaly detection pipeline and predictive analytics pipeline solutions is higher throughput of insights and also serving multiple streams of inference across the shop floor, thereby maximizing the potential of the IIoT AI solution. Popular and relevant IIoT AI use cases can realize significant performance improvement when software and AI implementations are purposefully optimized for the target AI hardware and systems.

MONITORING THE EFFICIENCY OF EXISTING INDUSTRIAL INTERNET OF THINGS SOLUTIONS

This article discusses how new technological options for industrial enterprises provide a simple approach to solving production problems and achieving economic growth. The main goal of this study is to analyze the effectiveness of existing industrial Internet of Things solutions, to determine the current state of technology and its relevance in the current crisis period. In its most general form, the implementation of the IIoT strategy involves connecting sensors and automated systems located in complex manufacturing and process locations to create a single data network. The study presents the general architectural elements of the industrial Internet of Things ecosystem and considers the IIoT and IT-OT system convergence system in the form of a multi-level scheme, explains the fundamental difference between IoT and IIoT technologies. Using the example of European countries, the level of technology integration at enterprises was analyzed, depending on the size and implementation goals. Depending on the size of the enterprise, there can be significant differences in the use of IIoT, so in large enterprises the studied technologies are used almost twice as much. It was determined that the largest share of investments falls on the sphere of security provision and support - 72% on average. It is noted that Covid-19 had the greatest positive impact on the accelerated implementation of IIoT technologies. The main benefits obtained by processing and analyzing data from smart sensors and sensors are described, and their application to specific goals is explored. At the same time, the main problems facing business and the government are listed. The impact of the Ukrainian-Russian conflict on the technological sector was analyzed, which led to problems with the logistics of important elements for the production of semiconductor microcircuits and expanded the list of possible risks from the introduction of digital technologies into production. Overall, growth in the number of connected devices slowed in 2021, but is expected to accelerate again in 2022 and beyond. Although new hurdles have emerged in the IoT market, the overall sentiment in the industry remains relatively positive.

Matrix profile implementation perspective in Industrial Internet of Things production maintenance application

The matrix profile processing is considered for the implementation of production maintenance tasks in the context of data acquisition by industrial Internet of Things solutions. The prospective implementation of the matrix profile data structure is verified through a dedicated case study, presenting the method of processing non-labelled data registered by sensors. The case study demonstrates the functionality of the profile and indicates the prospects of their applications in the field of production maintenance.

A Collaboratively Developed Platform to Introduce Fundamentals of IoT and IIoT

Industrial Internet of Things (IIoT) is a core technology of Industry 4.0 capable of providing transformative data and insights. Successful implementation of IIoT requires knowledge bridging the Information Technology (IT) and Operational Technology (OT) domains. This work presents a collaborative effort between multiple industry partners and academia to provide a comprehensive, cost-effective orientation to IIoT scoped for an undergraduate engineering audience. The outcome is a scalable solution that allows for the presentation and development of core skills focused on IoT concepts, cloud, edge, on-premises, hybrid IIoT solutions, data fundamentals, monitoring, control, analysis, and data applications utilizing IIoT systems and devices. Presentations also focus on demonstrating the best methods for securely deploying IIoT technologies in hybrid architectures. In addition to serving as a pathway to safely prepare and upskill students before they engage with industrial-scale systems in the learning factory, the platform also serves as a foundation for a new undergraduate curriculum for preparing graduates for Industry 4.0 and the digital transformation of the manufacturing industry.

Fairness-Based Packing of Industrial IoT Data in Permissioned Blockchains

In recent years, blockchain has been broadly applied to industrial Internet of Things (IIoT) due to its features of decentralization, transparency, and immutability. In existing permissioned blockchain based IIoT solutions, transactions submitted by IIoT devices are arbitrarily packed into blocks without considering their waiting times. Hence, there will be a high deviation of the transaction response times, which is known as the lack of fairness. Unfair permissioned blockchain decreases the quality of experience from the perspective of the IIoT devices. Moreover, some transactions can get timeouts if not responded for a long time. In this article, we propose Fair-Pack, the first fairness-based transaction packing algorithm for permissioned blockchain empowered IIoT systems. First, we gain the insight that fairness is positively related to the sum of waiting times of the selected transactions through theoretical analysis. Based on this insight, we transform the fairness problem into the subset sum problem, which is to find a valid subset from a given set with subset sum as large as possible. However, it is time consuming to solve the problem using a brute-force approach because there is an exponential number of subsets for a given set. To this end, we propose a heuristic and a min-heap-based optimal algorithm for different parameter settings. Finally, we analyze the time complexity of Fair-Pack and conduct extensive experiments. The results reveal that Fair-Pack is time-efficient and outperforms the existing algorithms significantly in terms of both fairness and average transaction response time.

Methodology for Indoor Positioning and Landing of an Unmanned Aerial Vehicle in a Smart Manufacturing Plant for Light Part Delivery

Unmanned aerial vehicles (UAV) are spreading their usage in many areas, including last-mile distribution. In this research, a UAV is used for performing light parts delivery to workstation operators within a manufacturing plant, where GPS is no valid solution for indoor positioning. A generic localization solution is designed to provide navigation using RFID received signal strength measures and sonar values. A system on chip computer is onboarded with two missions: first, compute positioning and provide communication with backend software; second, provide an artificial vision system that cooperates with UAV’s navigation to perform landing procedures. An Industrial Internet of Things solution is defined for workstations to allow wireless mesh communication between the logistics vehicle and the backend software. Design is corroborated through experiments that validate planned solutions.

Energy-Efficient End-to-End Security for Software-Defined Vehicular Networks

One of the most promising application areas of the industrial Internet of Things (IIoT) is vehicular ad hoc networks (VANETs). VANETs are largely used by intelligent transportation systems to provide smart and safe road transport. To reduce the network burden, software-defined networks (SDNs) act as a remote controller. Motivated by the need for greener IIoT solutions, this article proposes an energy-efficient end-to-end security solution for software-defined vehicular networks (SDVNs). Besides, SDN's flexible network management, network performance, and energy-efficient end-to-end security scheme plays a significant role in providing green IIoT services. Thus, the proposed SDVN provides lightweight end-to-end security. The end-to-end security objective is handled in two levels: 1) in roadside unit (RSU)-based group authentication scheme, each vehicle in the RSU range receives a group ID-key pair for secure communication; and 2) in private collaborative intrusion detection system (p-CIDS), the SDVN detects the potential intrusions inside the VANET architecture using collaborative learning that guarantees privacy through a fusion of differential privacy and homomorphic encryption schemes. The SDVN is simulated in NS2 and MATLAB, and results show increased energy efficiency with lower communication and storage overhead than existing frameworks. In addition, the p-CIDS detects the intruder with an accuracy of 96.81% in the SDVN.

Data-based process analysis in machining production: Case study for quality determination in a drilling process

The rush of new providers of Industrial Internet of Things solutions and machine learning applications onto the market opens up new possibilities for data acquisition and analysis that go beyond the classical approach of model- and empirical-based process analysis. In this context, classic production tasks, e.g. quality assurance by random sampling, should be critically reviewed for their relevance. These non-value-adding activities can potentially be eliminated by disruptive digitalisation in order to increase labour productivity. This paper showcases the potential of a data-driven approach for quality determination in a drilling process using machine control data.

A Novel Attack Detection Scheme for the Industrial Internet of Things Using a Lightweight Random Neural Network

The Industrial Internet of Things (IIoT) brings together many sensors, machines, industrial applications, databases, services, and people at work. The IIoT is improving our lives in several ways including smarter cities, agriculture, and e-healthcare, etc. Although the IIoT shares several characteristics with the consumer IoT, different cybersecurity mechanisms are adopted for both networks. Unlike consumer IoT solutions that are used by an individual user for a single purpose, IIoT solutions tend to be integrated into larger operational systems. As a result, IIoT security solutions require additional planning and awareness to ensure the security and privacy of the system. In this paper, different cybersecurity attacks such as denial of service (DoS), malicious operation, malicious control, data type probing, spying, scan, and wrong setup are predicted by applying machine learning techniques. To predict the aforementioned attacks, a novel lightweight random neural network (RaNN)-based prediction model has been proposed in this article. To investigate the performance of the RaNN-based prediction model, several evaluation parameters such as accuracy, precision, recall, and F1 score were calculated and compared with the traditional artificial neural network (ANN), support vector machine (SVM) and decision tree (DT). The evaluation results show that the proposed RaNN model achieves an accuracy of 99.20% for a learning rate of 0.01, with a prediction time of 34.51 milliseconds. Other performance parameters such as the precision, recall, and F1 score were 99.11%, 99.13%, and 99.20%, respectively. The proposed scheme improves the attack detection accuracy by an average of 5.65% compared to that of state-of-the-art machine learning schemes for IoT security.

Industrial Internet of Things Driven by SDN Platform for Smart Grid Resiliency

Software-defined networking (SDN) is a key enabling technology of industrial Internet of Things (IIoT) that provides dynamic reconfiguration to improve data network robustness. In the context of smart grid infrastructure, the strong demand of seamless data transmission during critical events (e.g., failures or natural disturbances) seems to be fundamentally shifting energy attitude toward emerging technology. Therefore, SDN will play a vital role on energy revolution to enable flexible interfacing between smart utility domains and facilitate the integration of mix renewable energy resources to deliver efficient power of sustainable grid. In this regard, we propose a new SDN platform based on IIoT technology to support resiliency by reacting immediately whenever a failure occurs to recover smart grid networks using real-time monitoring techniques. We employ SDN controller to achieve multifunctionality control and optimization challenge by providing operators with real-time data monitoring to manage demand, resources, and increasing system reliability. Data processing will be used to manage resources at local network level by employing SDN switch segment, which is connected to SDN controller through IIoT aggregation node. Furthermore, we address different scenarios to control packet flows between switches on hub-to-hub basis using traffic indicators of the infrastructure layer, in addition to any other data from the application layer. Extensive experimental simulation is conducted to demonstrate the validation of the proposed platform model. The experimental results prove the innovative SDN-based IIoT solutions can improve grid reliability for enhancing smart grid resilience.

Real-Time Manufacturing Machine and System Performance Monitoring Using Internet of Things

This paper introduces a framework to assess the performance of manufacturing systems using hybrid simulation in real time. Continuous and discrete variables of different machines are monitored to analyze performance using a virtual environment running synchronous to plant floor equipment as a reference. Data are extracted from machines using industrial Internet of Things solutions. Productivity and reliability of a physical system are compared in real time with data from a hybrid simulation. The simulation uses discrete-event systems to estimate performance metrics at a system level, and continuous dynamics at a machine level to monitor input and output variables. Simulation outputs are used as a reference to detect abnormal conditions based on deviations of real outputs in different stages of the process. This monitoring method is implemented in a fully automated manufacturing system testbed with robots and CNC machines. Machines are integrated on an Ethernet/IP control network using a programmable logic controller to coordinate actions and transfer data. Results demonstrated the capacity to perform real-time monitoring and capture performance errors within confidence intervals. Note to Practitioners —Estimating expected performance of a manufacturing system processing different parts across multiple machines is a complex problem due to the lack of closed-form equations. Existing solutions focus on monitoring stochastic variables such as production or failure rate, or machine dynamics in separate environments often running asynchronous to the real system. This paper addresses the problem of monitoring and assessing the performance of complex manufacturing systems in real time. The proposed framework uses a real-time hybrid simulation of manufacturing at a machine and system level. The hybrid approach is based on a discrete and continuous model of manufacturing equipment integrated to run synchronously with the real plant floor operation. Data from both the virtual and real environments are merged to assess performance. Deviations from expected values represent an error that can trigger a warning signal to production, maintenance, and/or manufacturing personnel at the plant regarding health and productivity of plant operations.

More Sustainability in Industry through Industrial Internet of Things?

Industrial production plays an important role for achieving a green economy and the sustainable development goals. Therefore, the nascent transformation of industrial production due to digitalization into a so-called Industrial Internet of Things (IIoT) is of great interest from a sustainable development point of view. This paper discusses how the environmental dimension of a sustainable development can potentially benefit from the IIoT—focusing especially on three topics: resource efficiency, sustainable energy and transparency. It presents a state of the art literature analysis of IIoT-enabled approaches addressing the three environmental topics. This analysis is compared with the findings of a survey among Chinese industrial companies, investigating the sustainability-related expectations of participants coming along with the implementation of IIoT solutions. China has been chosen as a case study because it brings together a strong industrial sector, ambitious plans regarding industrial digitalization and a high relevance and need for more sustainability. The survey was conducted with the means of a questionnaire which was distributed via email and used for direct on-site interviews. It focused on large and medium sized companies mainly from Liaoning Province and had a sample size of 109 participants.

Survey of business excellence by knowledge gathering for industrial internet-of-things applications

The internet-of-things (IoT) can change not only products but also companies' whole business model. The goal of this article is to inform companies about the creation of business intelligence via utilisation of the industrial IoT. This review article assumes that combining business excellence, service design and knowledge gathering via data engineering into cognitive infocommunication (CogInfoCom) opens new possibilities to develop business ideas. These new possibilities are examined through a literature review on deployments of industrial IoT solutions. The survey responds to two research questions: what methods of knowledge gathering and business excellence are used in industrial IoT-based product, service and process innovations? How can knowledge gathering, business excellence and CogInfoCom methods be utilised to create added value in industrial IoT solutions? This survey reveals the richness and delicacy of these exploitation opportunities.