Abstract

The Industrial Internet of Things (IIoT) and Digital Twins (DTs) are changing how digital models and physical products interact. IIoT connects to intelligence in the physical world, and DTs virtually represent their physical counterparts. As a result, DTs will be invaluable for testing and simulating new parameters and design variants. However, Despite the undeniable potential of DTs, they still cannot differentiate themselves from simulation technologies, and their application and adoption remain limited. This study defines the concept, highlights the evolution and development of DTs, reviews its key enabling technologies, identifies IIoT’s role as the backbone of DTs, examines DTs trends, highlights the key challenges, and explores its applications in the manufacturing process and Industry 4.0.

Introduction

The industrial sector has experienced a significant transformation due to several factors, the most notable being the shift in how products are sold, from the factory floor to the point of sale [1]. Over the past 15 years, there has been a growing trend among consumers to embrace e-commerce and digital channels for their purchasing needs [2], [3] Buyers’ habits changed to home ordering–a shift that industry experts forecast to be permanent. This consumer shift brought changes to supply chain disruptions — a dramatic need for increased agility and resilience in the consumer product manufacturing industries. As a result, manufacturers incorporated robust digital platforms in a post-pandemic marketplace to maintain competitiveness, optimize their manufacturing value chain, and provide end-to-end supply chain visibility [4], [5]. According to a new survey, manufacturing companies leveraging digital technologies to transform their operations have reduced costs by 5–30 percent, increased productivity by 5–40 percent, and achieved substantial agility and sustainability improvements [6].

The second factor having a correlating impact on industrial companies’ business models is COVID-19. The pandemic has led to significant disruptions affecting nearly every aspect of global industry [4]. While supply chain disruptions have uncovered operational vulnerabilities, they have also presented transformative opportunities for manufacturing companies [7]. Manufacturers quickly adapted during disruptions by implementing new advanced technologies at the core of true fourth industrial revolution innovation [6]. The swift advancement of digital technology and designed intelligence has driven the 4th industrial revolution [8]. Similarly, Industry 4.0 helped advance the usage of technologies like the Industrial Internet of Things (IIoT) and DT and enabled progress and betterment in the industries that utilize DT  [9], [10], [11].

Summary and conclusions

In the post-pandemic, manufacturers use digital transformation to gain much-needed agility by improving supply-chain visibility, creating feasible production plans, deploying advanced production planning and scheduling, and providing visibility, transparency, and traceability to consumer demand. We analyzed the use of DT and showed that DT technology with IoT capabilities has emerged as a critical concept in the digital transformation of the industry. It is used in Industry 4.0 and connected digital factories. The construction of DTs can be tailored for various objectives, including the conceptualization, production, examination, modeling, and management of non-digital systems. This is done to comprehend, supervise, and potentially enhance the real system. This paper explored the possibilities, challenges, and limitations of DT implementation and its use in different manufacturing domains. Research concerning DT solutions in manufacturing deals with production planning and control, the primary data sync within a production system that ties everything together. Supply chain and logistics management is another area where use cases of DT are reviewed in the literature.

DTs have been effectively utilized across various application fields, positioning them as a crucial component in Industry 4.0. Combining DTs and IIoT can enable new business models, such as predictive or product-as-a-service maintenance. In addition, with the ability to monitor and analyze data from multiple sources in real-time, companies can offer new value-added services to their customers. Overall, implementing DTs and IIoT can unlock significant benefits for industries regarding operational efficiency, cost savings, and new business opportunities.

Abstract

     A fundamental expectation of the stakeholders from the Industrial Internet of Things (IIoT) is its trustworthiness and sustainability to avoid the loss of human lives in performing a critical task. A trustworthy IIoT-enabled network encompasses fundamental security characteristics, such as trust, privacy, security, reliability, resilience, and safety. The traditional security mechanisms and procedures are insufficient to protect these networks owing to protocol differences, limited update options, and older adaptations of the security mechanisms. As a result, these networks require novel approaches to increase trust-level and enhance security and privacy mechanisms. Therefore, in this article, we propose a novel approach to improve the trustworthiness of IIoT-enabled networks. We propose an accurate and reliable supervisory control and data acquisition (SCADA) network-based cyberattack detection in these networks. The proposed scheme combines the deep-learning-based pyramidal recurrent units (PRU) and decision tree (DT) with SCADA-based IIoT networks. We also use an ensemble-learning method to detect cyberattacks in SCADA-based IIoT networks. The nonlinear learning ability of PRU and the ensemble DT address the sensitivity of irrelevant features, allowing high detection rates. The proposed scheme is evaluated on 15 datasets generated from SCADA-based networks. The experimental results show that the proposed scheme outperforms traditional methods and machine learning-based detection approaches. The proposed scheme improves the security and associated measure of trustworthiness in IIoT-enabled networks.

Introduction

     THE Industrial Internet of Things (IIoT) is a pervasive network that connects a diverse set of smart appliances in the industrial environment to deliver various intelligent services. In IIoT networks, a significant amount of industrial control systems (ICSs) premised on supervisory control and data acquisition (SCADA) are linked to the corporate network through the Internet [1]. Typically, these SCADA-based IIoT networks consist of a large number of field devices [2], for instance, intelligent electronic devices, sensors, and actuators, connected to an enterprise network via heterogeneous communications [3]. This integration provides the industrial networks and systems with supervision and a lot of flexibility and agility [2]–[4], resulting in greater production and resource efficiency. On the other hand, this integration exposes SCADA-based IIoT networks to serious security threats and vulnerabilities, posing a significant danger to these networks and the trustworthiness of the systems [5]. The trustworthiness of an IIoT-enabled system ensures that it performs as expected while meeting a variety of security requirements, including trust, security, safety, reliability, resilience, and privacy [6]–[8]. Fig. 1 depicts the fundamental aspects of trustworthiness in an IIoT-enabled network. The basic goal of the IIoT-enabled system is to increase trustworthiness by safeguarding identities, data, and services, and therefore to secure SCADA-based IIoT networks from cybercriminals [8], [9].

Conclusion

     The ability to protect SCADA-based IIoT networks against cyberattacks increases their trustworthiness. The existing security methods along with machine learning algorithms were inefficient and inaccurate for protecting IIoT networks. In this article, we proposed a cyberattacks detection mechanism using enhanced deep and ensemble learning in a SCADA-based IIoT network. The proposed mechanism is reliable and accurate because an ensemble detection model was built using a combination of the PRU and the DT. The proposed method was evaluated across 15 datasets generated from a SCADA-based network, and a considerable increase in terms of classification accuracy was obtained. Compared to state-of-the-art techniques, the obtained outcomes of our method exhibited a good balance between reliability, trustworthiness, classification accuracy, and model complexity, resulting in improved performance.

     In the future, we will employ more powerful deep learning models to further improve trustworthiness by detecting cyberattacks accurately. In addition, we will try to formulate and assess its performance in real-world scenarios. Also, we will work on the selection of optimal features in scenarios when the features are not sufficient.

Abstract

Industry 4.0, often known as the Industrial Internet of Things (IIoT), offers manufacturing businesses tremendous financial prospects and challenges. In this empirical study (based on an inductive qualitative research approach), we combined a literature review and an expert interview to look at the latest advancements in emerging technologies for manufacturing transformation and the requirements and approaches that manufacturing firms in emerging economies must employ to complete this transformation. Based on our findings, we developed a thorough overview of factors associated with IIoT and identified areas for additional study to contribute to the IIoT transformation agenda. The results of this study can guide stakeholders, particularly managers of manufacturing businesses and researchers. The literature study results posit state-of-the-art studies on how the implementation of IIoT technologies can drive the transformation of manufacturing businesses in emerging economies.

Introduction

The Internet of Things (IoT) paradigm has important applications in industrial environments. The Industrial Internet of Things (IIoT), also known as Industry 4.0, is an emerging technology that can revolutionize manufacturing and production by using a significant number of networked embedded sensing devices and integrating cutting-edge computing technologies [1,2].

As a result, managers globally are boosting their IT investment significantly [3]. Businesses’ existence may be dependent on their usage of new technology. According to literature, new technology is assisting businesses in improving their performance and driving more sustainable solutions [3,4]. The increased use and dependence on digital technology have resulted in substantial economic and social development changes. By linking goods and services to digitized business opportunities, these technologies may help organizations become more competitive. For example, changes in manufacturing processes, distribution, and firm organizational structures have occurred due to technological advancements in production and resources management [5–8]. Companies may use digital technology to enhance resource flow, value creation, and capital attraction in real-time, allowing for business transformation. Companies might also apply digital technology to optimize and transform their operations, such as managing inventory, supply chain, fabrication, etc., [3,4]. Importantly, digital technology enables businesses to reinvent how producers and consumers interact and can be achieved by enhancing consumer awareness and trying to track materials [9]. Industry 4.0 include increasing human-machine interaction, tracking, and self-recognition of components via intelligent robots and optimizing manufacturing based on Internet of Things (IoT) connectivity [10]. The concept of IoT gained popularity after the Hannover Fair in 2011 as a nascent engineering strategy.

Conclusion 

Our objective for this paper was to determine how IIoT transformation can impact manufacturing enterprise performance, focusing on emerging economies, reviewing available literature, and gathering insights from experts on the same topic. Additionally, we aimed to understand the aspects of the IIoT transition by identifying opportunities, challenges, and requirements for manufacturing and research. We conducted an academic and practitioner-focus literature review on the scope of the study. We cross-linked the themes to facilitate an expert interview with professionals in the field to support our findings. The experts confirm that although IIoT is gaining prominence in diverse industries, it is observed that CEOs and IT managers in manufacturing are uncertain about how to go about with the challenges of digitalization and so have become reluctant to take actions that can support their business to grow.

Moreover, many IIoT initiatives lack the requisite structures, processes, and capabilities to generate business innovations. Furthermore, they are frequently seen as being too bureaucratic, inflexible, and not consistent with their business model. The argument for IIoT transformation is that companies that want to progress in the evolving digital era must exploit the implementation of digital technology, rethink their business strategies and transform their operations. Three main areas of focus for organizations transforming into digital enterprises:

Abstract

     The ongoing expansion of the Industrial Internet of Things (IIoT) is enabling the possibility of effective Industry 4.0, where massive sensing devices in heterogeneous environments are connected through dedicated communication protocols. This brings forth new methods and models to fuse the information yielded by the various industrial plant elements and generates emerging security challenges that we have to face, providing ad-hoc functions for scheduling and guaranteeing the network operations. Recently, the large development of Software-Defined Networking (SDN) and Artificial Intelligence (AI) technologies have made feasible the design and control of scalable and secure IIoT networks. This paper studies how AI and SDN technologies combined can be leveraged towards improving the security and functionality of these IIoT networks. After surveying the state-of-the-art research efforts in the subject, the paper introduces a candidate architecture for AI-enabled Software-Defined IIoT Network (AI-SDIN) that divides the traditional industrial networks into three functional layers. And with this aim in mind, key technologies (Blockchain-based Data Sharing, Intelligent Wireless Data Sensing, Edge Intelligence, Time-Sensitive Networks, Integrating SDN&TSN, Distributed AI) and improve applications based on AI-SDIN are also discussed. Further, the paper also highlights new opportunities and potential research challenges in control and automation of IIoT networks.

Introduction

     Currently, we are witnessing an industrial revolution ranging term as Industry 4.0. Although this term may not be fully correct and involves a certain level of hype, the generalized implementation of autonomous decision making systems has become a new industry per se. The industry 4.0 is built by integrating networking capabilities that interconnect Artificial Intelligence (AI) agents in industrial plants, leading to the paradigms of Industrial Internet of Things (IIoT) and smart manufacturing [1,2]. These subjects are attracting continuous attention from both academia and industry, and have been categorized in various prototype models [3,4].

     Fig. 1 displays a typical architecture for IIoT networks, integrating four functional layers: data sensing, data transfer, data processing, and application layers [5,6]. The data sensing layer utilizes information acquisition and sensing technologies to collect heterogeneous data with different industrial attributes, and uploads them to the data transfer layer through advanced communication technologies, e.g., WiFi, Bluetooth, Internet of Things (IoT), 5G, etc. [7,8]. As the intermediated layer, the data transfer layer (based on the Internet backbone) aims at uploading the industrial data to the data processing layer by deploying Traffic Engineering (TE) or data routing policies, according to the given requirements and constraints (e.g., Quality of Service (QoS), costs, lags, etc.) of the industrial data delivery services [9]. Based on the dedicated data computing schemes (e.g., cloud/edge computing, distributed computing), the data processing layer adopts data fusion algorithms to process the data in real-time, and it provides an open interface for the application layer, such that the diverse categories of industrial applications can be edited and deployed at the application layer according to the current needs [10].

Conclusion

     Although the paradigm of IIoT networks promotes the progress of smart manufacture towards achieving reduced costs and increased efficiency, IIoT networks present the constraints of network heterogeneity and scalability. Current network architectures integrate computing units of low-intelligence, which cannot support the scheduling for diverse and complex industrial flow – especially for case scenarios with delay-sensitivity requirements. In this paper, we argued that these issues can be addressed by utilizing an AI-enabled SDN technique, and we have presented a formal architecture named by AI-SDIN to achieve this vision. Within the presented architecture, the IIoT networks are divided into three functional layers: data layer, network layer, and control layer. In the latter, advanced machine intelligence can be deployed to improve the security and functionality of the network. Then, we have surveyed possible key technologies towards actual implementation – e.g., Blockchain-based data sharing, intelligent wireless data sensing, edge intelligence, TSN&SDN, distributed AI, etc. We have also presented potential enhanced industrial applications based on AI-SDIN ranging from logistics to agriculture. Finally, the paper has indicated challenges and areas where further research is required. Our paper aims at revealing that the AI-SDIN leads the future of smart manufacture to be clear and bright. With AI-SDIN, industrial manufacture will be supermatic with less manufacture time and intelligent logistics, supporting fast production and marketing.

Abstract

Keywords

1. Introduction

2. What is the industrial Internet of Things (IIoT)

3. Need of IIoT

4. Research objectives

5. Different supporting technologies of IIoT

6. Major benefits of IIoT in manufacturing

7. Collective features of IIoT for Manufacturing Domain

8. Smart Transformations made into manufacturing through IIoT culture

9. Applications of IIoT in the field of manufacturing

10. Discussion

11. Limitations and future scope

12. Conclusion

Conflicts of interest

Declaration of competing interest

References

Abstract

Now a day’s manufacturing has become more intelligent and data-driven. A smart production unit can be thought of as a powerful connected industrial system with materials, parts, equipment, tools, inventory, and logistics that can transmit data and communicate with one another in the age of the Industrial Internet of Things (IIoT). This IIoT refers to linked devices, sensors, and other equipment that may be networked in the industrial environment to provide remote access, effective monitoring, better data collecting, analysis, & exchange etc. In Industry 4.0, IIoT is fundamental to transforming cyber-physical systems and production processes through big data and analytics. This paper provides an overview of the IIoT and the technologies that underpin it. The primary benefits and features of IIoT in manufacturing are discussed in detail. Smart Transformations made into the manufacturing field through IIoT Culture are discussed diagrammatically. Finally, twenty-nine significant applications of IIoT in the field of manufacturing are identified and discussed. IIoT can monitor the transport, supply of the goods, consult details on things in warehouses, and check the conditions related to product storage and delivery and allow all dispersed and outsourced operations to be monitored. Therefore, the industry is being revolutionised by IIoT, altering the way industrial enterprises function daily to improve efficiency and performance levels.

1. Introduction

Industrial Internet of Things (IIoT) is the next level of Internet of Things (IoT) technology and is unique in its manufacturing transformation. Companies seeking a competitive edge today need just look at the capabilities that IIoT can offer the advantages all from maintenance, logistics providers, workflows for employees, and product delivery. IIoT enables manufacturers to digitise almost all parts of their businesses. Manufacturers can decrease the main risks associated with manual work and human error by minimising hand processes and entries. IIoT can help decrease cyber risk and human mistake data violations. There are hundreds of sensors that generate data in a typical industrial plant [[1], [2], [3]]. With IIoT, producers can proactively enhance performance by recognising possible bottlenecks, breakdowns, and production process gaps. Combining data from a site network may also contribute to more effective management of material flows early detection and identification of production, and optimising machinery and equipment performance at all installations [4,5].

Abstract

Introduction

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Abstract

Cloud manufacturing represents a service-oriented manufacturing paradigm that allows ubiquitous and on-demand access to various customisable manufacturing services in the cloud. While a vast amount of research in cloud manufacturing has focused on high-level decision-making tasks, such as service composition and scheduling, the link between field-level manufacturing data and the cloud manufacturing platform has not been well established. Efficient data acquisition, communication, storage, query, and analysis of field-level manufacturing equipment remain a significant challenge that hinders the development of cloud manufacturing systems. Therefore, this paper investigates the implementation of the emerging Industrial Internet of Things (IIoT) technologies in a cloud manufacturing system to address this challenge. We propose a service-oriented plug-and-play (PnP) IIoT gateway solution based on a generic system architecture of IIoT-supported cloud manufacturing system. Service-oriented data schemas for manufacturing equipment are developed to capture just-enough information about field-level manufacturing equipment and allow efficient data storage and query in a cloud time-series database (TSDB). We tested the feasibility and advantages of the proposed approach via the practical implementation of the IIoT gateways on a 3D printer and a machine tool.

Introduction

Cloud manufacturing represents a service-oriented manufacturing paradigm where manufacturing resources are virtualised as manufacturing services that can be managed and configured in an intelligent and unified way in the cloud and allows ubiquitous and on-demand network access [1], [2], [3]. Cloud manufacturing aims to achieve efficient integration and sharing of distributed manufacturing resources to enable efficient on-demand production of highly customised products. As a promising trend of the future of manufacturing, ever since the introduction of the concept of cloud manufacturing in 2010 [4], various research topics related to cloud manufacturing such as architecture design, resource virtualisation, service selection and composition, service searching and matching, and task scheduling have been widely discussed and studied in both academia and industry during the last decade [5]. However, despite the vast amount of research effort in this field, one has to admit that the envisioned cloud manufacturing paradigm has not been achieved yet.

Abstract

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Abstract:

COVID-19 has disrupted normal life and has enforced a substantial change in the policies, priorities and activities of individuals, organisations and governments. These changes are proving to be a catalyst for technology and innovation. In this paper, we discuss the pandemic’s potential impact on the adoption of the Internet of Things (IoT) in various broad sectors, namely healthcare, smart homes, smart buildings, smart cities, transportation and industrial IoT. Our perspective and forecast of this impact on IoT adoption is based on a thorough research literature review, a careful examination of reports from leading consulting firms and interactions with several industry experts. For each of these sectors, we also provide the details of notable IoT initiatives taken in the wake of COVID-19. We also highlight the challenges that need to be addressed and important research directions that will facilitate accelerated IoT adoption.

1. Introduction

The Internet of Things (IoT) is envisioned as a network of billions of devices that can sense, communicate and share data which can then be analyzed to unlock a wealth of intelligence useful for planning, management and decision making [1]. IoT promises huge benefits for a variety of domains such as healthcare, manufacturing, agriculture, telecommunication and transportation. Despite the popularity of the IoT concept and its promised benefits, its adoption has been significantly slower than expected [2,3]. Some of the major reasons behind this include: (1) security, privacy, policy and trust issues [3–5]; (2) organisational inertia, long capital cycles and shortage of specialist workforce needed to successfully implement IoT [2,5]; and (3) lack of convincing use cases with clear return on investment (ROI) in some sectors.

COVID-19 has impacted all walks of life so much that we may never return to the old normal. This pandemic is proving to be a catalyst for digital transformation because COVID19 has created or expanded applications and use cases of digital technologies [6–8]. It has also forced governments, organisations and individuals to change/adapt their priorities, their views on societal/ethical issues, and the way they operate. In many cases, this has addressed or mitigated many of the above mentioned reasons behind the slower-thanexpected adoption of IoT across many verticals. For example, governments have invested huge amounts of resources in IoT and other technologies to combat COVID-19. Lifestyle changes brought about by COVID-19 such as work/study from home have also provided new use cases of IoT with clear ROI such as remote asset control, workforce tracking and remote employee collaboration [9]. Consequently, many organisations have increased investment in IoT and the pace of their IoT projects [10,11]. The fight against COVID-19 has resulted in a less strict stance on privacy issues, higher trust in technology and fasttracked approval procedures. This is also paving the way for accelerated adoption of IoT in various verticals. Furthermore, regulatory changes such as stricter cleaning and tracking requirements for businesses are also accelerating the IoT adoption in smart buildings.

I- INTRODUCTION

II- IOT, IIOT AND INDUSTRY 4.0

III- STATE OF THE ART

IV- OPPORTUNITIES AND CHALLENGES

REFERENCES

Abstract

Internet of Things (IoT) is an emerging domain that promises ubiquitous connection to the Internet, turning common objects into connected devices. The IoT paradigm is changing the way people interact with things around them. It paves the way to creating pervasively connected infrastructures to support innovative services and promises better flexibility and efficiency. Such advantages are attractive not only for consumer applications, but also for the industrial domain. Over the last few years, we have been witnessing the IoT paradigm making its way into the industry marketplace with purposely designed solutions. In this paper, we clarify the concepts of IoT, Industrial IoT, and Industry 4.0. We highlight the opportunities brought in by this paradigm shift as well as the challenges for its realization. In particular, we focus on the challenges associated with the need of energy efficiency, real-time performance, coexistence, interoperability, and security and privacy. We also provide a systematic overview of the state-of-the-art research efforts and potential research directions to solve Industrial IoT challenges.

Introduction

Internet of Things (IoT) is a computing concept describing ubiquitous connection to the Internet, turning common objects into connected devices. The key idea behind the IoT concept is to deploy billions or even trillions of smart objects capable to sense the surrounding environment, transmit and process acquired data, and then feedback to the environment. It is expected that by the year 2021 there will be around 28 billion connected devices [1]. Connecting unconventional objects to the Internet will improve the sustainability and safety of industries and society, and enable efficient interaction between the physical world and its digital counterpart, i.e. what is usually addressed as a Cyber-physical System (CPS). IoT is usually depicted as the disruptive technology for solving most of present-day society issues such as smart cities, intelligent transportation, pollution monitoring, connected healthcare, to name a few. As a subset of IoT (see Fig. 1), Industrial IoT (IIoT) covers the domains of machine-to-machine (M2M) and industrial communication technologies with automation applications. IIoT paves the way to better understanding of the manufacturing process, thereby enabling efficient and sustainable production. Flexibility and scalability required by IoT communications are typically addressed using wireless links. In the past, wireless technologies in industrial applications were mostly based on ad hoc solutions, e.g. individually developed for connecting moving parts or hard-to-reach devices. Only recently, standards purposely designed for the industry (e.g., WirelessHART [2] and ISA100.11a [3]) were released. However, they face limitations in terms of scalability and coverage when very large areas need to be covered. While cellular technologies such as 3/4/5G technologies promise to connect massive devices over long distances, they require infrastructure support and licensed band [4]. IIoT applications typically require relatively small throughput per node and the capacity is not a main concern. Instead, the need of connecting a very large number of devices to the Internet at low cost, with limited hardware capabilities and energy resources (e.g. small batteries) make latency, energy efficiency, cost, reliability, and security/privacy more desired features [5]. Meeting the above mentioned requirements poses a number of key challenges on the evolution of IIoT. Addressing these challenges is critical in order to ensure a massive roll-out of IIoT technologies. In this paper, we clarify the concepts of IoT, IIoT, and the current trend of automation and data exchange in manufacturing technologies called Industry 4.0. We highlight the opportunities brought in by IIoT as well as the challenges for its realization.

Historically, Industrial Automation and Control Systems (IACS) were largely isolated from conventional digital networks such as enterprise ICT environments. Where connectivity was required, a zoned architecture was adopted, with firewalls and/or demilitarized zones used to protect the core control system components. The adoption and deployment of ‘Internet of Things’ (IoT) technologies is leading to architectural changes to IACS, including greater connectivity to industrial systems. This paper reviews what is meant by Industrial IoT (IIoT) and relationships to concepts such as cyber-physical systems and Industry 4.0. The paper develops a definition of IIoT and analyses related partial IoT taxonomies. It develops an analysis framework for IIoT that can be used to enumerate and characterise IIoT devices when studying system architectures and analysing security threats and vulnerabilities. The paper concludes by identifying some gaps in the literature.

Introduction

The concept of Industrial Automation and Control Systems (IACS) is well established. These systems, often referred to as Operational Technology (OT), are employed in diverse industries including manufacturing, transportation and utilities, and are sometimes referred to as cyber-physical systems (CPS). Since the term Internet of Things (IoT) [1] was first used in 1999, it has been applied to connected devices in consumer, domestic, business and industrial settings [2]. Although there is a significant amount of literature attempting to define IoT, its uses, and its typical components, it is rarely made obvious how any of this applies in the industrial setting. Because current definitions of IoT invariably imply a similar approach to the high-level architecture of a system, the ubiquitous use of the term IoT to refer to the use of digital technologies in industry is unhelpful as it hinders the analysis of alternative system architectures, including the location and nature of the data or information processing, and associated performance and security issues. The aims of this paper are to improve on existing definitions of Industrial IoT (IIoT) and to propose a framework for IIoT components as a basis for analysing the use and deployment of IoT technologies in industrial settings. In undertaking this research our aim was to establish a framework that allows us to analyse the nature of IIoT devices and their uses, which is to be used as part of a vulnerability and threat analysis process for these devices. By being able to characterise the devices in a systematic manner, we anticipate being able to analyse cross-cutting threats and vulnerabilities and identify patterns that may be obscured when focusing on the technology employed or sector specific issues. This paper is structured as follows: Section 2 provides some further background to CPS, IACS, and the Industrial Internet, setting it against the background of Industry 4.0. Section 3 provides our analysis and definition of IIoT, which builds on existing explanations that are presented. Section 4 presents our framework. Finally, Section 5 identifies gaps in the current literature that need to be addressed in future work.

The Internet of Things (IoT) is one of the hottest topics in communications today. Although the forecast of 50 billion devices by 2020 may be outdated, the general trend that early analysts predicted is undeniable. Current values vary from 6 to 9 billion devices, whereas forecasts estimate from 20 to 30 billion IoT devices for 2020 (e.g., Ericsson’s figure is 28 billion for 2021) [1]. Gartner [2] has forecast that Industrial IoT (IIoT) devices will represent around 37 percent of the global number, which will account for about 57 percent of overall IoT spending in 2017. Although there is no formal definition [3], an IEEE report describes IoT as “a network of items — each embedded with sensors — which are connected to the Internet.” Considering the type and the purpose of the things connected, IoT systems can be classified into consumer, industrial, and manufacturing. Consumer IoT systems connect things that consumers utilize such as wearable devices, home automation, and security devices, or for healthcare. Their purpose is to improve the users’ quality of life. Industrial IoT systems connect things that basically are non-consumer (i.e., things used by professionals and companies). IIoT use cases encompass industrial machinery, transportation monitoring, logistic tracking, asset tracking, healthcare, intelligent buildings, smart cities, smart agriculture, and smart metering. Their purpose is to increase productivity and reduce the environmental impact. Manufacturing IoT systems are focused on factories in order to optimize their processes (smart manufacturing). The precise industries that are covered under IIoT depend on the approach of the different industry bodies. For example, in many cases the terms Industrie 4.0 and IIoT are used interchangeably, but the former is restricted to manufacturing.1 However, the Industrial Internet2 Consortium considers the following industries as the major IIoT vertical markets: energy, healthcare, manufacturing, smart cities, and transportation.