Efficient plaintext checkable identity‐based signcryption in cyber‐physical systems towards IIoT

Recent advancements of the Industrial Internet of Things (IIoT) have revolutionized modern urbanization and smart cities. While IIoT data contain rich events and objects of interest, processing a massive amount of IIoT data and making predictions in real-time are challenging. Recent advancements in artificial intelligence (AI) allow processing such a massive amount of IIoT data and generating insights for further decision-making processes. In this article, we propose several key aspects of AI-enabled IIoT data for smart city monitoring. First, we have combined a human-intelligence-enabled crowdsourcing application with that of an AI-enabled IIoT framework to capture events and objects from IIoT data in real time. Second, we have combined multiple AI algorithms that can run on distributed edge and cloud nodes to automatically categorize the captured events and objects and generate analytics, reports, and alerts from the IIoT data in real time. The results can be utilized in two scenarios. In the first scenario, the smart city authority can authenticate the AI-processed events and assign these events to the appropriate authority for managing the events. In the second scenario, the AI algorithms are allowed to interact with humans or IIoT for further processes. Finally, we will present the implementation details of the scenarios mentioned above and the test results. The test results show that the framework has the potential to be deployed within a smart city.

SummaryThrough the Industrial Internet of Things (IIoT), the manufacturing enterprises have significantly enhanced the production efficiency. The transmission of data in IIoT over public channels has brought about information leakage issues. Therefore, it is crucial to agree on a session key among the participants. In recent years, numerous authenticated key exchange (AKE) schemes have been designed. However, those AKE schemes which adopt the certificateless approach to tackle certificate management and key escrow may remain vulnerable to quantum attacks, and other AKE schemes which are based on lattice hard problem so as to achieve the post quantum security may incur key management issues. To simultaneously address the aforementioned challenges, we propose a lightweight certificateless lattice‐based authenticated key exchange (C2LAKE) scheme. Upon the hardness assumptions of the inhomogeneous small integer solution (ISIS) and computational bilateral inhomogeneous small integer solution (CBi‐ISIS) problems, the C2LAKE scheme has been demonstrated to be secure in the eCK model and the random oracle model (ROM). Better trade‐off among security and functionality features, and communication and computation costs make the C2LAKE scheme suitable and applicable in the IIoT environment.

The industrial Internet of things (IIoT) is a framework that enables companies to improve their processes and increase their efficiency by using smart sensors and actuators. It is also used to collect data from various industrial devices and make use of this data to improve their operations. With the help of connected sensors and actuators, companies can save time. Due to the increasing number of industrial Internet of things (IIoT) devices, the data generated by these networks is constantly being analyzed. However, due to the high dimensionality of the data, it can cause fragmentation. Also, datasets collected by IIoT nodes are prone to displaying anomalous events. Unlike traditional networks, which are usually composed of several applications and protocols, IIoT networks are different. They require more stringent security measures to protect the integrity and confidentiality of their data. Due to the unique features of IIoT networks, they are prone to attack. This paper briefly describes the various requirements and challenges faced when it comes to protecting these networks. It also presents evaluation of various machine learning classifiers to detect anomalous behaviors in these networks. Due to the nature of the data that these networks contain anomalies, their analysis is critical to prevent attacks. In this paper, we analyze the usage of various machine learning algorithms to check the efficiency and accuracy of detecting anomalies in data generated by crucial IIoT devices.

The rapid integration of industrial automation and control systems into large-scale Industrial Internet of Things (IIoT) infrastructures has substantially increased system complexity. The complexity arises from the deployment of heterogeneous devices that must coordinate in real-time to support adaptive decision-making. As a result, a robust situation awareness (SA) framework for IIoT has become a critical goal of IIoT. This review investigates the role of SA in the design of controllers in industrial applications, with the aim of advancing control strategies and facilitating integration with emerging IoT technologies. The survey focuses on SA, Model Predictive Control (MPC), and their integration, based on time series as a standard data structure to represent device performance. A key finding from the current literature is that SA is often treated separately from control theory, resulting in isolated applications and not integrated with large-scale IIoT systems. This survey establishes the features and paradigms of SA in IIoT. It provides a structured analysis of controller design methods in IIoT. State-space models (SSMs) can be used to model temporal dynamics in IIoT data. Finally, the integration of artificial intelligence (AI) with SSMs is explored to achieve SA based on multivariate time series data.

The evolution of the Industrial Internet of Things (IIoTs) greatly increases the volume of data generated by the connected IIoT devices. IIoT data are playing an increasingly important role in various industrial sectors. IIoT data sharing helps enterprises make better production decisions and respond to market changes timely. However, the distrust among IIoT entities and IIoT entities' distrust of data-sharing platforms may hinder the realization of data sharing. In this article, a decentralized IIoT data-sharing scheme based on blockchain and edge computing is proposed. A Proof of Storage and Transmission (PoST) consensus mechanism is proposed to meet data storage and transmission requirements of data owners in IIoT data-sharing networks. Based on the manufacture ties of data owners, shared data request probabilities are derived. The IIoT data sharing interactions between data owners and edge devices are modeled as a multiple-leader and multiple-follower Stackelberg game. The alternating direction method of multipliers (ADMMs) algorithm is used to obtain the optimal IIoT data sharing solutions in a distributed manner. Simulation results show that compared with the cooperative scheme, the total profit of edge devices is maximally increased by 59%, and the total utility of data owners is maximally increased by 52%.

Energy efficiency plays a crucial role in the field of industrial automation, particularly in relation to conveyor belt systems that operate continuously in various sectors, including mining, logistics, and manufacturing. Smart Industrial Components (SIC) are required to process, manage, and produce a substantial amount of different types of IIOT (Industrial Internet of Things) data. This historical data is the main source for analysing the current and future conditions of the components. In this paper, a predictive analytics algorithm is proposed to analyse the trend of real-time and future power consumption. The proposed algorithm seamlessly combines generative AI with IIoT data to enhance predictive modelling capabilities. This study highlights key challenges, methodologies, and future directions in leveraging AI-driven analytics for industrial energy management, as well as investigates the application of predictive data analytics for conveyor belt systems focusing on two key aspects: (1) historical data analysis to monitor the current condition of the conveyor belt system and (2) the use of generative AI for future prediction essential for analysing future trends of power consumption running at the specific condition.

The rapid development of the supply chain of the Internet of Things (IoT) industry may trigger financial risk contagion among IoT manufacturers. This article collects data on listed IoT companies in the Chinese market from 2010 to 2019 and explores the development of the environment for the IoT industry. Two dynamic time wrapping (DTW) networks are created to analyze the topological structures of the IoT industry environment. Both the standard DTW and strongly connected DTW networks are revolutionary in terms of their interconnectedness in the IoT industry. We found that the level of clustering and transitivity of the network continued to decline between 2010 and 2019; i.e., the efficiency of financial risk contagion on IoT networks was significantly reduced. This article contributes to the literature in two aspects. First, it reveals that China's IoT industry is increasingly competitive; financial risks have become more difficult to transfer. The IoT industry has become more robust and exhibits a lower likelihood of financial risk contagion. Second, the article provides empirical evidence for the theory of coevolution, showing that risk contagion ability in an industry setting is decreasing with the development of individual firms.

The growing requirement for the rise of production, quality, and efficiency of industrial products drives humans to jointly develop novel technologies capable of following up with the exponential technology growth they are facing today in production processes. In the 21st century, industrial internet of things (IIoT) is dedicated to endorsing the internet of things (IoT) to allow the interconnection of anywhere, any time, and anything to enhance efficiency, productivity, intelligence, and safety in the manufacturing system. Consequently, the combined IoT and cyber physical system (CPS) in IIoT will have an influence on industrial techniques through the industrial 4.0 confluence to encourage connected factories. CPS effectively combines physical and cyber components employing modern components such as sensors, network, and computing technologies. But various securities challenges rising in the CPS, like increasing cyberattacks due to the increase in IoT devices, modelling of security threads, CPS vulnerabilities assessment development techniques, and designing of fault-tolerant architectures, motivate new technologies to meet CPS requirements in order to offer security, confidentiality, and reliability of users’ data. Thus, this chapter presents the classification of various existing studies based on CPS security in terms of IIoT applications. Also, it provides the limitations, shortcomings, CPS architectures, and future potential applications to help researchers in the context of practical work.

Efficient heterogeneous signcryption scheme based on Edge Computing for Industrial Internet of Things

The Internet of Things (IoT) is an emerging and even revolutionary technology with an immense impact on our everyday life. The Industrial Internet of Things (IIoT) is a fast-growing technology that increases productivity and efficiency by combining the IoT platform with an existing industrial environment. IIoT networks are one of the main pillars for the success of the future Industry 4.0 and Cyber-Physical System (CPS) paradigm. The IIoT ecosystem represents a network of connected industrial devices that exchange and analyze collected data to enable new insights into industrial processes. Recently, intensive research activities have focused on cybersecurity issues for the IIoT. This chapter addresses the critical components of the IIoT security framework by analyzing the relevant aspects and providing an overview of state-of-the-art activities in this field. This chapter also discusses the IIoT architectural structure, applications, and underlying networking technologies, with a particular focus on security challenges and standardization activities.

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.

The evolution of the Internet of Things (IoT) has empowered modern industries with the capability to implement large-scale IoT ecosystems, such as the Industrial Internet of Things (IIoT). The IIoT is vulnerable to a diverse range of cyberattacks that can be exploited by intruders and cause substantial reputational and financial harm to organizations. To preserve the confidentiality, integrity, and availability of IIoT networks, an anomaly-based intrusion detection system (IDS) can be used to provide secure, reliable, and efficient IIoT ecosystems. In this paper, we propose an anomaly-based IDS for IIoT networks as an effective security solution to efficiently and effectively overcome several IIoT cyberattacks. The proposed anomaly-based IDS is divided into three phases: pre-processing, feature selection, and classification. In the pre-processing phase, data cleaning and normalization are performed. In the feature selection phase, the candidates’ feature vectors are computed using two feature reduction techniques, minimum redundancy maximum relevance and neighborhood components analysis. For the final step, the modeling phase, the following classifiers are used to perform the classification: support vector machine, decision tree, k-nearest neighbors, and linear discriminant analysis. The proposed work uses a new data-driven IIoT data set called X-IIoTID. The experimental evaluation demonstrates our proposed model achieved a high accuracy rate of 99.58%, a sensitivity rate of 99.59%, a specificity rate of 99.58%, and a low false positive rate of 0.4%.

SummaryThe evolution of Internet of Things (IoT) has led to the development of Industrial Internet of Things (IIoT). IIoT is one the widely applied areas to facilitate people in the manufacturing world. The adoption of IIoT automates sensing, capturing, communicating, and processing in real time. To understand how rapidly IoT and IIoT are growing, this article examines the emergence of 5G‐enabled IIoT, current research trends in IIoT, key milestones achieved in IIoT, and IoT applications specific to 5G‐enabled IIoT. The paper presents the state‐of‐the‐art in networking layered framework of IIoT and comparing relationships of technologies of cloud computing as well as edge computing paradigms. We also explored the type of security attacks and their preventive measures in an IIoT‐driven 5G technology. We have also highlighted the revolution of IIoT‐driven 5G framework which satisfies the demands of IIoT applications.

The industrial internet of things (IIoT) is progressing owing to intervention as technology of Industry 4.0, which requires devices to be connected to the Internet in industrial systems. These are based on the efficient management and monitoring of data using a network between PLC S7-1200 and Simatic IoT2040, through an IIoT, which stores, processes, and transfers data within the Freeboard web platform. However, an important aspect is to compare the response time of processed data between a real-time mechatronic task and interface IIoT. The system used is OPC-UA for the transmission and reception of data from the devices used in the production, the IoT tool Node-Red in the gateway-connected OPC-UA client and Freeboard web platform, permitting the analysis and visualization of data in real time, guaranteeing greater assertiveness in decision-making. The average communication delay time from the production line to the Cloud ranges from 2 ms to about 10 ms.KeywordsIIoTIndustry 4.0OPC-UAMQTTPLC S7-1200Simatic IoT2040FreeboardReal-Time MonitoringIndustrial Internet of ThingsCloud platformNode-RedTIA Portal softwareMindSphere

This position article presents dispersed automation, a novel approach for reprogramming the omnipresent industrial Internet of Things (IIoT) devices in critical infrastructure for the co-execution of general-purpose computation workloads in an optimal and reliable manner. The key observation is that IIoT devices are available in large quantities in critical infrastructure. However, these devices are often underutilized due to the conservative allocation of resources, static task assignment, and lack of exploitation of parallelism. Dispersed automation gives new life to IIoT devices, some of them as powerful as general-purpose computers, and makes them accessible for the co-execution of various computational workloads, while dynamically adapting to different applications and operational environments. Two key contributions make this possible: the use of a domain-specific language and on-channel computation. We present dispersed automation as an enabling technology for the implementation of future smart and adaptive IIoT systems. Note to Practitioners —In this article, we explain our concept and vision on how programs can be created and designed in a way that makes it possible to run them on IIoT networks with a greater amount of flexibility than we have today. With this technology, it will be possible for programs to adapt to changes in their operation, and to changes in the network environment, in order to better utilize the available hardware resources. This means that a dispersed automation system can continue its operation in certain situations where a traditionally deployed system would fail due to insufficient resources (e.g., memory, processing speed). It also means that the software can make better use of the underlying hardware whenever a special task needs to be carried out (e.g., a “heavy” diagnostic process). We show some experimental results of using our approach based on simulation. The way forward is to implement and evaluate this technology in an industrial setting.