Industrial Internet Of Things Context Research Articles

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

This paper presents an innovative solution to the key exchange problem in the Industrial Internet of Things (IIoT) implementation. Communication between connected devices must be safe and effective if the IIoT is to continue its fast growth. A crucial part of safeguarding the security and integrity of data shared across the IIoT ecosystem is played by cryptographic key exchange approaches. To provide reliable cryptographic key exchange in the IIoT context, this paper offers a unique approach that makes use of hyperchaotic with complex values, variable parameters, and vector-valued neural synchronization. The method addresses the continued demand for effective cryptographic key exchange across IIoT devices by using drive–response techniques to speed up important applications. Long assessment times in conventional algorithms make it difficult to hide neuronal coordination. As a result, this paper offers a condensed evaluation of ANNs’ (Artificial Neural Networks) synchronization by coordinating ANNs for session key exchange using a hyperchaotic setting. The recommended approach has several benefits, including the following: (1) introducing a hyperchaotic system to generate synchronized input vectors for ANN synchronization; (2) The adaptive rules of parameters-based control procedures are constructed mathematically; (3) reciprocal alignment of vector-valued ANNs to form a neural network for establishing session keys throughout the IIoT network; and (4) relevant numerical simulations are carried out to assess the scheme’s consistency. The recommended approach performs better than other methods that have been published in the literature, widening up opportunities for more effective and reliable industrial applications.

OPC UA TSN: a next-generation network for Industry 4.0 and IIoT

PurposeThe purpose of this paper is to describe how emerging open standards are replacing traditional industrial networks. Current industrial Ethernet networks are not interoperable; thus, limiting the potential capabilities for the Industrial Internet of Things (IIoT). There is no forthcoming new generation fieldbus standard to integrate into the IIoT and Industry 4.0 revolution. The open platform communications unified architecture (OPC UA) time-sensitive networking (TSN) is a potential vendor-independent successor technology for the factory network. The OPC UA is a data exchange standard for industrial communication, and TSN is an Institute of Electrical and Electronics Engineers standard for Ethernet that supports real-time behaviour. The merging of these open standard solutions can facilitate cross-vendor interoperability for Industry 4.0 and IIoT products.Design/methodology/approachA brief review of the history of the fieldbus standards is presented, which highlights the shortcomings for current industrial systems in meeting converged traffic solutions. An experimental system for the OPC UA TSN is described to demonstrate an approach to developing a three-layer factory network system with an emphasis on the field layer.FindingsFrom the multitude of existing industrial network schemes, there is a convergence pathway in solutions based on TSN Ethernet and OPC UA. At the field level, basic timing measurements in this paper show that the OPC UA TSN can meet the basic critical timing requirements for a fieldbus network.Originality/valueThis paper uniquely focuses on the specific fieldbus standards elements of industrial networks evolution and traces the developments from the early history to the current developing integration in IIoT context.

Rotating machinery fault diagnosis based on a novel lightweight convolutional neural network.

The advancement of Industry 4.0 and Industrial Internet of Things (IIoT) has laid more emphasis on reducing the parameter amount and storage space of the model in addition to the automatic and accurate fault diagnosis. In this case, this paper proposes a lightweight convolutional neural network (LCNN) method for intelligent fault diagnosis of rotating machinery, which can largely satisfy the need of less parameter amount and storage space as well as high accuracy. First, light-weight convolution blocks are constructed through basic elements such as spatial separable convolutions with the aim to effectively reduce model parameters. Secondly, the LCNN model for the intelligent fault diagnosis is constructed via lightweight convolution blocks instead of the tradi-tional convolution operation. Finally, to address the “black box” problem, the entire network is visualized through Tensorboard and t-distribution stochastic neighbor embedding. The results demonstrate that when the number of lightweight convolutional blocks reaches 6, the diagnosis accuracy of the LCNN model exceeds 99.9%. And the proposed model has become the most robust with parameters significantly decreasing. Furthermore, the proposed LCNN model has realized accurate, automatic, and robust fault diagnosis of rotating machinery, which makes it more suitable for deployment under the IIoT context.

On the Adoption of Physically Unclonable Functions to Secure IIoT Devices

The growing convergence among information and operation technology worlds in modern Industrial Internet of Things (IIoT) systems is posing new security challenges, requiring the adoption of novel security mechanisms involving light architectures and protocols to cope with IIoT devices resource constraints. In this article, we investigate the adoption of physically unclonable functions (PUFs) in the IIoT context, and propose the design of a PUF-based architecture (Pseudo-PUF), obtained by suitably combining a weak PUF and an encryption module, that can be successfully adopted to implement advanced security primitives while meeting the existing requirements of IIoT devices in terms of cost and resource demand. To demonstrate the feasibility of our proposal, we analyzed the overall quality of different Pseudo-PUF instances with respect to well-known PUF quality metrics, and found that it is possible to obtain good results with a negligible impact on the devices, thus making our approach suited to IIoT deployments.

On deep reinforcement learning security for Industrial Internet of Things

The Industrial Internet of Things (IIoT), also known as Industry 4.0, empowers manufacturing and production processes by leveraging automation and Internet of Things (IoT) technologies. In IIoT, the information communication technologies enabled by IoT could greatly improve the efficiency and timeliness of information exchanges between both vertical and horizontal system integrations. Likewise, machine learning algorithms, particularly Deep Reinforcement Learning (DRL), are viable for assisting in automated control of complex IIoT systems, with the support of distributed edge computing infrastructure. Despite noticeable performance improvements, the security threats brought by massive interconnections in IoT and the vulnerabilities of deep neural networks used in DRL must be thoroughly investigated and mitigated before widespread deployment. Thus, in this paper we first design a DRL-based controller that could be deployed at edge computing server to enable automated control in an IIoT context. We then investigate malicious behaviors of adversaries with two attacks: (i) function-based attacks that can be launched during training phase and (ii) performance-based attacks that can be launched after training phase, to study the security impacts of vulnerable DRL-based controllers. From the adversary’s perspective, maximum entropy Inverse Reinforcement Learning (IRL) is used to approximate a reward function through observation of system trajectories under the control of trained DRL-based controllers. The approximated reward function is then used to launch attacks by the adversary against the Deep Q Network (DQN)-based controller. Via simulation, we evaluate the impacts of our two investigated attacks, finding that attacks are increasingly successful with increasing accuracy of the control model. Furthermore, we discuss some tradeoffs between control performance and security performance of DRL-based IIoT controllers, and outline several future research directions to secure machine learning use in IIoT systems.

A reliability and performance GSPN-Based model for anti-collision RFID algorithms under noisy channels in industrial internet of things

The Industrial Internet of Things (IIoT) has the potential to deeply change the technological landscape in factories, transforming the way automation procedures are performed and relevant objects are identified. For that, however, reliability and performance issues have to be considered when providing the expected communication services. When employing Radio-Frequency Identification (RFID) in an IIoT context, previous works have struggled to improve the efficiency of RFID-complaint systems, usually defining mathematical models for planning and quality assessment. Nevertheless, such models are designed based on error-free communications, which are unreal when considering the error-prone nature of wireless communications in industrial plants. Therefore, this article defines a new formal model based on Generalized Stochastic Petri Nets (GSPN) for RFID communications, modelling different possibilities for errors between readers and RFID tags. Since this proposal employs the EPCglobal UHF Class 1 Gen2 parameters as reference, which are already adopted by the Dynamic Frame Slotted Aloha anti-collision protocol for passive RFID systems, this model can be exploited to assess the reliability and performance of different RFID medium access protocols when assuming noisy channels, supporting better comparisons among different algorithms and protocols. Simulation scenarios are defined to present reliability and performance results when assessing RFID tags reading, which are valuable when designing and maintaining IIoT applications.

OPC UA Publish-Subscribe and VSOME/IP Notify-Subscribe Based Gateway Application in the Context of Car to Infrastructure Communication.

With the recent advances in the area of OPC UA interfacing and the continuously growing requirements of the industrial automation world, combined with the more and more complex configurations of ECUs inside vehicles and services associated to car to infrastructure and even car to car communications, the gap between the two domains must be analyzed and filled. This gap occurred mainly because of the rigidness and lack of transparency of the software-hardware part of the automotive sector and the new demands for car to infrastructure communications. The issues are related to protocols as well as to conceptual views regarding requirements and already adopted individual directions. The industrial world is in the Industry 4.0 era, and in the Industrial Internet of Things context, its key interfacing enabler is OPC UA. Mainly to accommodate requirements related, among others, to high volumes, transfer rates, larger numbers of nodes, improved coordination and services, OPC UA enhances within its specifications the Publish-Subscribe mechanism and the TSN technology. In the OPC UA context, together with the VSOME/IP Notify-Subscribe mechanism, the current work is stepping toward a better understanding of the current relation between the needs of the industry and the suitable technologies, providing in-depth analysis on the most recent paradigms developed for data transmission, taking in consideration the real-time capabilities and use-cases of high concern in automation and automotive domains, and toward obtaining a VSOME/IP—OPC UA Gateway that includes the necessary characteristics and services in order to fill the protocol-related gap between the above mentioned fields. The developed case study results are proving the efficiency of the concept and are providing a better understanding regarding the impact between ongoing solutions and future requirements.

Enhanced Lightweight Multiscale Convolutional Neural Network for Rolling Bearing Fault Diagnosis

The vibration signals collected from rolling bearings in industrial systems are highly complex and contain intense environmental noise, which challenges the performance of traditional fault diagnosis methods. Moreover, the applicability of the model in engineering practice, especially in the Industrial Internet of Things context, puts forward higher requirements for its storage and computational costs. Considering these challenges, this article proposes an enhanced lightweight multiscale convolutional neural network (CNN) for rolling bearing fault diagnosis. Our contributions mainly fall into three aspects. Firstly, the proposed model is modular and easy to expand, which combines the idea of multiscale learning with attention mechanism and residual learning, enabling the network to extract more abundant and discriminative fault features directly from the raw vibration signal. Consequently, the proposed model can perform better. Secondly, the interpretability of the multiscale learning mechanism is explored by visualizing the extraction process of multiscale features. Finally, for the first time, we introduce the depthwise separable convolution into multiscale CNN to reduce the storage and computational costs of the model, which realizes the lightweight of the model and improves its applicability in the Industrial Internet of Things context. The experimental results on the rolling bearing dataset demonstrate that, compared with the state-of-the-art multiscale CNN models, the proposed model has better discriminative fault feature extraction ability and anti-noise ability, and is more suitable for practical industrial systems.

Bearing Intelligent Fault Diagnosis in the Industrial Internet of Things Context: A Lightweight Convolutional Neural Network

The advancement of Industry 4.0 and Industrial Internet of Things (IIoT) has laid more emphasis on reducing the parameter amount and storage space of the model in addition to the automatic and accurate fault diagnosis. In this case, this paper proposes a lightweight convolutional neural network (LCNN) method for intelligent fault diagnosis of bearing, which can largely satisfy the need of less parameter amount and storage space as well as high accuracy. First, depthiwise separable convolution is adopted, and a LCNN structure is constructed through an inverse residual structure and a linear bottleneck layer operation. Secondly, a novel decomposed Hierarchical Search Space is introduced to automatically explore the optimal LCNN for bearing fault diagnosis in the context of the IIoT. In the meantime, the real-time monitoring and fault diagnosis of the model are also deployed. In order to verify the validity of the designed model, Case Western Reserve University Bearing fault dataset and MFPT bearing fault dataset are adopted. The results demonstrate the great advantages of the model. The LCNN model can automatically learn and select the appropriate features, highly improving the fault diagnosis accuracy. Meanwhile, the computational and storage costs of the model are largely reduced, which contributes to its being applied to the mechanical system in the IIoT context.

A Distributed Service Framework for the Internet of Things

This article introduces distributed service framework (DSF), a distributed service framework supporting development, and deployment of trustworthy and privacy-preserving distributed Internet of Things (IoT) and Industrial IoT (IIoT) applications. DSF provides a common protocol and infrastructure for secure service specification, registration, discovery, publishing, and subscription, over insecure public networks, and introduces mechanisms for service replication, supporting dynamic scalability of services, and delegation, allowing use on embedded and resource-constrained devices. DSF is an open protocol, with an open-source implementation including supporting library for application development, a command-line client for user interaction, and a daemon that manages services and data. DSF is qualitatively evaluated against existing approaches to developing distributed applications and common technologies used in the IIoT context and demonstrated through the development of an IIoT scenario.

Defining a Cyber Resilience Investment Strategy in an Industrial Internet of Things Context.

The fourth industrial revolution has brought several risks to factories along with its plethora of benefits. The convergence of new technologies, legacy technologies, information technologies and operational technologies in the same network generates a wide attack surface. At the same time, factories need continuous production to meet their customers’ demand, so any stopped production can have harsh effects on a factory’s economy. This makes cyber resilience a key requirement in factories nowadays. However, it is difficult for managers to define effective cyber resilience strategies, especially considering the difficulty of estimating adequate investment in cyber resilience policies before the company has suffered cyber incidents. In this sense, the purpose of this article is to define and model an effective cyber resilience strategy. To achieve this, the system dynamics methodology was followed in order to get five experts’ opinions on the best strategy to invest in cyber resilience. Interviews were conducted with these experts; their reasoning was put into behavior over time graphs and a system dynamics model was built from these findings. The main conclusion is that a cyber resilience investment strategy should be dynamic, investing in both technical security and personnel training, but at first with an emphasis on technical security and later shifting to have an emphasis on training.

A Survey on Industrial Internet of Things: A Cyber-Physical Systems Perspective.

The vision of Industry 4.0, otherwise known as the fourth industrial revolution, is the integration of massively deployed smart computing and network technologies in industrial production and manufacturing settings for the purposes of automation, reliability, and control, implicating the development of an Industrial Internet of Things (I-IoT). Specifically, I-IoT is devoted to adopting the Internet of Things (IoT) to enable the interconnection of anything, anywhere, and at anytime in the manufacturing system context to improve the productivity, efficiency, safety and intelligence. As an emerging technology, I-IoT has distinct properties and requirements that distinguish it from consumer IoT, including the unique types of smart devices incorporated, network technologies and quality of service requirements, and strict needs of command and control. To more clearly understand the complexities of I-IoT and its distinct needs, and to present a unified assessment of the technology from a systems perspective, in this paper we comprehensively survey the body of existing research on I-IoT. Particularly, we first present the I-IoT architecture, I-IoT applications (i.e., factory automation (FA) and process automation (PA)) and their characteristics. We then consider existing research efforts from the three key systems aspects of control, networking and computing. Regarding control, we first categorize industrial control systems and then present recent and relevant research efforts. Next, considering networking, we propose a three-dimensional framework to explore the existing research space, and investigate the adoption of some representative networking technologies, including 5G, machine-to-machine (M2M) communication, and software defined networking (SDN). Similarly, concerning computing, we again propose a second three-dimensional framework that explores the problem space of computing in I-IoT, and investigate the cloud, edge, and hybrid cloud and edge computing platforms. Finally, we outline particular challenges and future research needs in control, networking, and computing systems, as well as for the adoption of machine learning, in an I-IoT context.

Privacy Protection for Data-Driven Smart Manufacturing Systems

The Industrial Internet of Things (IIoT) is a new industrial ecosystem that combines intelligent and autonomous machines, advanced predictive analytics, and machine-human collaboration to improve productivity, efficiency and reliability. The integration of industry and IoT creates various attack surfaces and new opportunities for data breaches. In the IIoT context, it will often be the case that data is considered sensitive. This is because data will encapsulate various aspects of industrial operation, including highly sensitive information about products, business strategies, and companies. The transition to more open network architectures and data sharing of IoT poses challenges in manufacturing and industrial markets. The loss of sensitive information can lead to significant business loss and cause reputational damage. In this paper, the authors discuss emerging issues that are related to IIoT data sharing, investigate possible technological solutions to hide sensitive information and discuss some privacy management techniques in smart manufacturing systems.

Delay-aware and reliability-aware contention-free MF–TDMA protocol for automated RFID monitoring in industrial IoT

Abstract This paper presents a multi-frequency time division multiple access (MF–TDMA) protocol for Radio Frequency IDentification (RFID) monitoring system in industrial Internet of Things (IoT). Unlike critical industrial control system which requires 1–2 ms response in communication, the monitoring system in industries normally demands a guaranteed delay in hundred-millisecond level. Under the context of IoT, massive items are monitored, the requirements of the monitoring system thus further include reliable communication, low-power low-cost flexible hardware structure, scalable system infrastructure, and quality of service (QoS) on demand. To fulfill these, we employ a flexible hardware infrastructure which uses a centralized coordinator and a set of readers to support multiple frequency channels or technologies to maximize the system capacity. The proposed MF–TDMA protocol provides a scheduled communication both in time and frequency with an optional automatic repeat request (ARQ), independent/uniform synchronization and control mechanism, and an optimization of time slot allocation to increase the transmission reliability while decreasing the latency to a guaranteed bound. The performance evaluation indicates that based on the proposed MF–TDMA protocol, the system can promise more than 95% of the tags to enjoy than 50 ms waiting time. And a delay bound of 100 ms is achieved when a 16-byte packet is transmitted at 256 kbps. Moreover, 100% packet transmission ratio can always be obtained thanks to the schedule and optimization mechanism.