Industrial Internet Of Things Services Research Articles

Dealing with applications in Industrial IoT

Industrial Internet of Things (IIoT) also known as Industry 4.0, consists of industries that utilize IoTs to facilitate logistics, manufacturing, transportation, oil and gas, energy/utilities, mining and metals, aviation, and others. Though a lot of companies have proceeded into a lot of areas utilizing IIoT, its applicability in a lot of is still not explored and remains unpredictable. Its evolution is similar to that of the Internet in the early 90s, where it initially appeared and slowly gained momentum in the 20th century to almost becoming and indispensable aspect of human life in the 21st century. Robotics and control are already a part of most of the manufacturing companies, however, when it comes to real-time applications in manufacturing, IIoT may not come to the rescue. Another aspect that separates the internet and IIoT is that the internet is based on the bet effort delivery model which is acceptable for e-commerce and man-machine interactions. However, failures in an IIoT scenario such as air-traffic monitoring, automated manufacturing can have serious consequences. Industry 4.0 is an initiative taken up by industries to use IoT in industries and many such initiatives have been taken up by different companies and countries. For example, GE has already invested $1billion to improve its asset performance and business operation by using IIoT services. Germany has started an initiative called “Industry 4.0”, a strategic initiative that brought together leaders from private and public sectors as well as from academia to create a detailed plan of action for incorporating digital technologies into German industries. Other countries such as Italy, Netherlands, and France have invested in setting up smart factories and industries. China has also started a project called “Made in China 2025” to integrate digital technologies and industrialization. A major hindrance in adopting IIoT is that the smart devices often speak different “languages” that existing industrial automation software uses, which creates a communication gap, and hence standardization is necessary. Overcoming such bottlenecks and adopting IIoT can help automate the production techniques, for instance, manufacturers could increase their productivity by a margin of 30%. The capabilities of IIoT can be explored in areas such as sensor driven computing, industrial analytics, and intelligent machine applications. The main aim of this topic is to explain various applications inf IoT in industries.

A Fog-Based Security Framework for Large-Scale Industrial Internet of Things Environments

The Industrial Internet of Things (IIoT) is a developing research area with potential global Internet connectivity, turning everyday objects into intelligent devices with more autonomous activities. IIoT services and applications are not only being used in smart homes and smart cities, but they have also become an essential element of the Industry 4.0 concept. The emergence of the IIoT helps traditional industries simplify production processes, reduce production costs, and improve industrial efficiency. However, the involvement of many heterogeneous devices, the use of third-party software, and the resource-constrained nature of the IoT devices bring new security risks to the production chain and expose vulnerabilities to the systems. The Distributed Denial of Service (DDoS) attacks are significant, among others. This article analyzes the threats and attacks in the IIoT and discusses how DDoS attacks impact the production process and communication dysfunctions with IIoT services and applications. This article also proposes a reference security framework that enhances the advantages of fog computing to demonstrate countermeasures against DDoS attacks and possible strategies to mitigate such attacks at scale.

Strategies for Assuring Low Latency, Scalability and Interoperability in Edge Computing and TSN Networks for Critical IIoT Services

In recent years, there has been an exponential growth of IoT (Internet of Things) devices in the market and increasingly, they are being part of critical mission functionalities for many industries. For this reason, processes associated with these devices are generating new requirements at the level of interconnectivity, security, and scalability. Due to the large volume of devices, this growth process has tested the existing communications infrastructure, deployment architecture, different support services, technical requirements and the intrinsic characteristics of the services offered. In this sense, the use of Edge Computing schemes and TSN networks (Time Sensitive Networks), is proposed as a tool for supporting IIoT (Industrial Internet of Things) services to achieve optimization and assurance processes of latency, scalability and interoperability parameters required by critical mission services such as health, transportation and production. In this sense, we propose several strategies for assuring low latency, scalability and interoperability in Edge Computing and TSN networks for critical IIoT services. These strategies are evaluated considering different levels of congestion and considering different schemes such as best effort, TSN and Edge Computing.

Developing Robot-Assisted Telerehabilitation System

<h3>Research Objectives</h3> The aim is to develop a novel telerehabilitation system to deliver robot-assisted rehabilitation using IIoT (Industrial Internet of Things) PTC ThingWorx, and AR (Augmented Reality) Vuforia Studio platforms. <h3>Design</h3> Experimental Study. <h3>Setting</h3> In the BioRobotics Lab. <h3>Participants</h3> Three healthy participants, age: 23–35 years, height: 1.60–1.80 m, weights: 51–110 kg. <h3>Interventions</h3> Remotely administer a wide range of robot-assisted rehab therapy ranging from passive to resistive exercises using either a joystick or AR session. <h3>Main Outcome Measures</h3> We assessed the functionality and usability of the developed Telerehabilitation system configuring human-robot collaborative sessions through the use of IIoT and AR-based user interface which enables the developed telerehabilitation system to provide various robot-assistive rehabilitation exercises. <h3>Results</h3> Results demonstrated the developed robot-assisted telerehabilitation system that can successfully and effectively follow the trajectories as commands passed by the therapist using an IIoT services and AR-based user interface and physical joystick. We conducted experiments on healthy subjects. The therapist monitored trajectories of robotic arm though digital twin on the Vuforia Studio, and feedback is recorded. Experiment results ensure that the robotic arm is used to provide various rehabilitation therapies for upper extremity movements at low latency. <h3>Conclusions</h3> Developed robot-assisted telerehabilitation system is mainly focused to provide individuals with physical and/or cognitive disabilities, including muscle weakness, spasms, and multi-joint coordination problems with remote assistance and evaluation. This system minimizes the distance between rehabilitative patients and therapists. By using this system, patients will regain the ability to lead normal daily lives. <h3>Author(s) Disclosures</h3> The authors have no conflicts of interest to declare.

Elastic O-RAN Slicing for Industrial Monitoring and Control: A Distributed Matching Game and Deep Reinforcement Learning Approach

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.

SPTM-EC: A Security and Privacy-Preserving Task Management in Edge Computing for IIoT

Data/tasks of Industrial Internet of Things (IIoT) are extremely private and valuable to computing. Edge computing facilitates IIoT services by offering platforms of supportive facilities and functionalities. However, the convergence of intelligent edge computing and IIoT platforms has been held back by the need to develop systems for maintaining security and privacy. In this article, we propose a new framework for securely and confidentially sending, storing, and computing IIoT tasks. This framework employs a lightweight encryption scheme alongside modified ElGamal encryption and digital signature schemes. We analyzed the robustness of this framework in terms of security and privacy, and assessed its performance through simulations. Ultimately, the proposed framework outperformed the existing models in terms of time complexity, latency, and energy consumption.

Automatic whitelist generation system for ethernet based in-vehicle network

Owing to the development of industrial internet of things (IIoT) technology, the connectivity and complexity of vehicles have also increased, and new communication technologies have been introduced to in-vehicle networks (IVN). In order to achieve a sufficient level of IIoT cybersecurity, strict ground rules must exist in the critical infrastructures (CI). Traditionally, many legacy communication techniques such as controller area networks (CAN), and FlexRay have been proposed and used for IVN and CI. However, these legacy protocols cannot accommodate advanced IIoT technologies that require high connectivity; hence, Ethernet for vehicles has been introduced in recent years. The phenomenon of introducing Ethernet to IVN is an irreversible industrial trend, such as Ethernet-based Smart Factory, Smart City, and industrial control system based on IIoT. Some automotive Ethernet protocols such as MOST150 and BroadR-Reach have been commercialized primarily for infotainment and are being expanded for providing IIoT services. However, security studies pertaining to automotive Ethernet are incomplete. Even though the amount of data that must be processed by security solutions in the Ethernet environment is increasing, vehicles must be guaranteed ‘hard real-time’. In this study, to reflect the characteristics of IVN security such as real-time, extensibility, and certainty, we establish an IVN simulation environment based on several Ethernet protocols and propose an automatic whitelist generation system. The whitelist performs filtering based on certain criteria, and after it is defined, the time consumed for packet preprocessing is extremely small. The proposed system is designed to create a whitelist through learning to ensure extensibility in an IVN environment composed of heterogeneous networks. The proposed system operates in two stages: the first step automatically generates three types of whitelists, i.e., global, local, and connection, by learning the network; the second step performs filtering by applying the generated rule. The whitelists generated through the proposed system filter out abnormal packets or sections based on the alert levels. The proposed system is expected to cope flexibly with various potential cyber threats on IVNs in the future.

Cloud–Edge Collaborative SFC Mapping for Industrial IoT Using Deep Reinforcement Learning

The industrial Internet of Things (IIoT) and 5G have been served as the key elements to support the reliable and efficient operation of Industry 4.0. By integrating burgeoning network function virtualization (NFV) technology with cloud computing and mobile edge computing, an NFV-enabled cloud–edge collaborative IIoT architecture can efficiently provide flexible service for the massive IIoT traffic in the form of a service function chain (SFC). However, the efficient cloud–edge collaboration, the reasonable comprehensive resource consumption, and different quality of services are still key problems to be solved. Thus, to balance the quality of IIoT services, as well as computational and communicational resource consumption, a multiobjective SFC deployment model is designed to characterize the diverse service requirements and specific network environment for the IIoT. Then, a deep- <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$Q$</tex-math></inline-formula> -learning-based online SFC deployment algorithm is presented, which can efficiently learn the relationship between the SFC deployment scheme and its performance through the iterative training. Simulation results demonstrate that our proposed approach outperforms others in balancing the resource consumption, accepting more SFC requests, as well as providing differentiated services for delay-sensitive IIoT traffic and resource-intensive IIoT traffic.

A Survey of Intelligent Network Slicing Management for Industrial IoT: Integrated Approaches for Smart Transportation, Smart Energy, and Smart Factory

Network slicing has been widely agreed as a promising technique to accommodate diverse services for the Industrial Internet of Things (IIoT). Smart transportation, smart energy, and smart factory/manufacturing are the three key services to form the backbone of IIoT. Network slicing management is of paramount importance in the face of IIoT services with diversified requirements. It is important to have a comprehensive survey on intelligent network slicing management to provide guidance for future research in this field. In this paper, we provide a thorough investigation and analysis of network slicing management in its general use cases as well as specific IIoT services including smart transportation, smart energy and smart factory, and highlight the advantages and drawbacks across many existing works/surveys and this current survey in terms of a set of important criteria. In addition, we present an architecture for intelligent network slicing management for IIoT focusing on the above three IIoT services. For each service, we provide a detailed analysis of the application requirements and network slicing architecture, as well as the associated enabling technologies. Further, we present a deep understanding of network slicing orchestration and management for each service, in terms of orchestration architecture, AI-assisted management and operation, edge computing empowered network slicing, reliability, and security. For the presented architecture for intelligent network slicing management and its application in each IIoT service, we identify the corresponding key challenges and open issues that can guide future research. To facilitate the understanding of the implementation, we provide a case study of the intelligent network slicing management for integrated smart transportation, smart energy, and smart factory. Some lessons learnt include: 1) For smart transportation, it is necessary to explicitly identify service function chains (SFCs) for specific applications along with the orchestration of underlying VNFs/PNFs for supporting such SFCs; 2) For smart energy, it is crucial to guarantee both ultra-low latency and extremely high reliability; 3) For smart factory, resource management across heterogeneous network domains is of paramount importance. We hope that this survey is useful for both researchers and engineers on the innovation and deployment of intelligent network slicing management for IIoT.

IoTactileSim: A Virtual Testbed for Tactile Industrial Internet of Things Services.

With the inclusion of tactile Internet (TI) in the industrial sector, we are at the doorstep of the tactile Industrial Internet of Things (IIoT). This provides the ability for the human operator to control and manipulate remote industrial environments in real-time. The TI use cases in IIoT demand a communication network, including ultra-low latency, ultra-high reliability, availability, and security. Additionally, the lack of the tactile IIoT testbed has made it more severe to investigate and improve the quality of services (QoS) for tactile IIoT applications. In this work, we propose a virtual testbed called IoTactileSim, that offers implementation, investigation, and management for QoS provisioning in tactile IIoT services. IoTactileSim utilizes a network emulator Mininet and robotic simulator CoppeliaSim to perform real-time haptic teleoperations in virtual and physical environments. It provides the real-time monitoring of the implemented technology parametric values, network impairments (delay, packet loss), and data flow between operator (master domain) and teleoperator (slave domain). Finally, we investigate the results of two tactile IIoT environments to prove the potential of the proposed IoTactileSim testbed.

Federated Learning for Industrial Internet of Things in Future Industries

The Industrial Internet of Things (IIoT) offers promising opportunities to revolutionize the operation of industrial systems and become a key enabler of future industries. Recently, artificial intelligence (AI) has been widely utilized for realizing intelligent IIoT applications where AI techniques require centralized data collection and processing. However, this is not always feasible in realistic scenarios due to the high scalability of modern IIoT networks and growing industrial data confidentiality. Federated Learning (FL), as an emerging collaborative AI approach, is particularly attractive for intelligent IIoT networks by coordinating multiple IIoT devices and machines to perform AI training at the network edge while helping protect user privacy and confidential business information. In this article, we provide a detailed overview and discussions of the emerging applications of FL in several key IIoT services and applications. A case study is also provided to demonstrate the feasibility of FL in IIoT. Finally, we highlight a range of interesting open research topics that need to be addressed for the full realization of FL-IIoT in future industries.

Formal Verification of Control and Operational Behaviors in Industrial Internet of Things Services Composition

Industrial Internet of Things (IIoT) services composition rely on composing existing IoT services in industrial context in order to obtain an overall service with added value. The composite service’s behavior is extremely influenced by the time, availability, and accuracy of the unitary services. Thus, it is extremely important to guarantee a provision of IIoT services as expected during the modeling phase. For this, we lie on formal verification to check all the possible scenarios before to pass through IIoT services provision. We propose first to model each process involved in the composition by open Time Petri Nets. These nets offer interface places for the purpose of process communication with the other processes. Then the composition of open Time Petri Nets is guaranteed via superimposing the interface places and thus obtaining a Time Petri Net modeling the composite process. Finally, control and operational behaviors of IIoT services composition formally checked after being specified in an expressive fragment of TCTL temporal logic

Trustworthiness assessment for industrial IoT as multilayer networks with von Neumann entropy

The Industrial Internet of Things (IIoT) has expanded worldwide rapidly, which brings key devices and applications of IIoT under a trustworthy umbrella that reinforces secure and safe IIoT services have never been more important. However, there are few effective methods for assessing the trustworthiness of IIoT networks and services, which may lead to a compromised system and massive decreases in productivity, or even catastrophic consequences. Complex networks have emerged to be a promising method to assess the trustworthiness of IIoT because they can reveal the latent features of networks and services. Enlightened by the potential of complex networks, a cloud-fog-edge computing paradigm for IIoT is presented and mapped to multilayer networks. Furthermore, we propose a Trustworthiness Assessment with Entropy (TAE) method, which quantitatively analyzes the topological characteristics of the IIoT networks and services. Experimental results on synthetic and real-world datasets present a comprehensive assessment of IIoT trustworthiness with the qualitative and quantitative analysis of von Neumann entropy, which proves the feasibility and robustness of the proposed method.

Intelligent-Driven Green Resource Allocation for Industrial Internet of Things in 5G Heterogeneous Networks

The Industrial Internet of Things (IIoT) is one of the important applications under the 5G massive machine type of communication (mMTC) scenario. To ensure the high reliability of IIoT services, it is necessary to apply an efficient resource allocation method under the dynamic and complex environment. In view of the absence of energy-efficient resource management architecture for the entire network, this article proposes an intelligent-driven green resource allocation mechanism for the IIoT under 5G heterogeneous networks. First, an intelligent end-to-end self-organizing resource allocation framework for IIoT service is given. Next, an energy-efficient resource allocation model within the framework is proposed. It is then solved by an intelligent mechanism with the asynchronous advantage actor critic driven deep reinforcement learning algorithm. Through the comparison analysis of different methods and rewards under IIoT scenarios with proper parameters setting, the proposed method can achieve better performance than other traditional deep learning (DL) methods and maintain service quality above accepted levels as well.

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.

AI-Inspired Non-Terrestrial Networks for IIoT: Review on Enabling Technologies and Applications

During the last few years, various Industrial Internet of Things (IIoT) applications have emerged with numerous network elements interconnected using wired and wireless communication technologies and equipped with strategically placed sensors and actuators. This paper justifies why non-terrestrial networks (NTNs) will bring the IIoT vision closer to reality by providing improved data acquisition and massive connectivity to sensor fields in large and remote areas. NTNs are engineered to utilize satellites, airships, and aircrafts, which can be employed to extend the radio coverage and provide remote monitoring and sensing services. Additionally, this paper describes indicative delay-tolerant massive IIoT and delay-sensitive mission-critical IIoT applications spanning a large number of vertical markets with diverse and stringent requirements. As the heterogeneous nature of NTNs and the complex and dynamic communications scenarios lead to uncertainty and a high degree of variability, conventional wireless communication technologies cannot sufficiently support ultra-reliable and low-latency communications (URLLC) and offer ubiquitous and uninterrupted interconnectivity. In this regard, this paper sheds light on the potential role of artificial intelligence (AI) techniques, including machine learning (ML) and deep learning (DL), in the provision of challenging NTN-based IIoT services and provides a thorough review of the relevant research works. By adding intelligence and facilitating the decision-making and prediction procedures, the NTNs can effectively adapt to their surrounding environment, thus enhancing the performance of various metrics with significantly lower complexity compared to typical optimization methods.

Unleash Narrowband Technologies for Industrial Internet of Things Services

As the industrial market grows, it is becoming noticeable that there are many industrial Internet of things (IIoT) use cases for which existing technology cannot meet the huge demand of machine connectivity. For example, in the utility market, there is a strong trend to adopt new technology that can support positive business use case scenarios for efficient system operation and elaborate the dramatic increase of the services demands. Apart from this, most utility grid applications required long-range, low-power, secure, and reliable communications, which means narrowband (NB) technology can be the dominant choice. To address these challenges, this article provides a new framework architecture to enable technical decision makers to plan for NB-IIoT. Moreover, we highlight the key aspects of NB technology by focusing on the challenges, standardization, and requirements to facilitate the IIoT connectivity for industry revolutions. The motivation behind employing NB is to provide a high level of reliability, and better quality of service, and coverage. In particular, the article addresses the main applications of utility use cases under the NB umbrella, which can perform as a good bridge between utility services and the fundamental communication infrastructure. The utility use cases based on emerging technology can support the full array of smart grid services that are required for both central and distributed operation systems. Finally, the article provides connectivity solutions for potential IIoT deployment aiming to define a new roadmap for NB technology on specific industrial use cases.

Service Popularity-Based Smart Resources Partitioning for Fog Computing-Enabled Industrial Internet of Things

Recently, fog computing has gained increasing attention in processing the computing tasks of the industrial Internet of things (IIoT) with different service popularity. In task-diversified fog computing-enabled IIoT (F-IIoT), the mismatch between expected computing efficiency and partitioned resources on fog nodes (FNs) may pose serious traffic congestion even large-scale industrial service interruptions. The existing works mainly studied offloading which type of computing tasks into FNs, but few studies enabled smart resource partitioning of FNs. In this paper, a service popularity-based smart resources partitioning (SPSRP) scheme is proposed for fog computing-enabled IIoT. We first exploit Zipf's law to model the relationship between popularity ranks and computing costs of IIoT services. Moreover, we propose an implementation architecture of the SPSRP scheme for F-IIoT, which decouples the computing control layer from data processing layer of IIoT through a specified SPSRP controller. Besides, a mobility and heterogeneity-aware partitioning algorithm is presented for extending SPSRP scheme to seamlessly support cross-domain resources partitioning. The simulations demonstrate that the SPSRP scheme can bring notable performance improvements on delay time, successful response rate and fault tolerance for fog computing to deal with the large-scale IIoT services.

Leveraging heterogeneous device connectivity in a converged 5G-IoT ecosystem

As mobile communications technology is completing its fifth-generation (5G) cycle, it becomes increasingly capable of supporting novel usage scenarios with stringent performance requirements. Beyond seamless broadband connectivity for humans, 5G systems are preparing to enable a wide range of machine-type applications, thus advancing the vision of the Internet of Things (IoT). Facilitated by the rapidly converging 5G-IoT ecosystem, next-generation industrial IoT services, however, pose unprecedented research problems, primarily along the lines of providing wireless connectivity with ubiquitous availability, extreme reliability, and ultra-low latency. To this end, the first part of this paper11The work of O. Galinina was supported in part with a personal research grant by the Finnish Cultural Foundation and in part by a Jorma Ollila grant from Nokia Foundation. The work of S. Andreev was supported in part by a Postdoctoral Researcher grant from the Academy of Finland and in part by a Jorma Ollila grant from Nokia Foundation. presents a concise update on the novel requirements and challenges in the context of the emerging Industrial Internet infrastructure. In the second part, we introduce and solve a new optimization problem formulation aimed at improving performance reliability for advanced IoT devices equipped with several radio access technologies. We argue that by intelligently leveraging such heterogeneous multi-connectivity, future 5G-grade IoT applications will be able to improve their levels of operational reliability. Our conclusions are corroborated by rigorous mathematical derivations as well as several realistic numerical examples.