Dynamic User Authentication Protocol for Industrial IoT without Timestamping

PurposeThe Internet of Things (IoT) provides exciting opportunities for the construction industry to solve its time and resource constraints and frequent defaults. This study seeks to identify and rank the perceived importance level of principal research areas associated with the IoT and the construction industry by utilising a scientific mapping tool (i.e. VOSviewer). Such knowledge would enable key drivers for successful adoption of the IoT and digitisation technologies to be outlined. An analysis of key drivers and research trends that facilitates the development of a roadmap for applying the IoT and digital technologies in the construction sector is therefore much needed.Design/methodology/approachAn interpretivist philosophical lens was adopted to analyse published work as secondary data, where each publication represented a unit of analysis. A total of 417 peer-reviewed journal review articles covering the IoT within the construction domain were systematically reviewed using a mixed-methods approach, utilising qualitative-scientometric analyses techniques.FindingsThe results revealed a field of study in a fledgling stage, with a limited number of experts operating somewhat in isolation and offering single-point solutions instead of taking an integrated “holistic” approach. Key publication outlets were identified and the main focus of research undertaken being in the technical areas of smart buildings, smart construction objects and environmental sustainability. The major effects of adopting the IoT within the construction industry were identified as high-speed reporting, complete process control, data explosion leading to deep data analytics, strict ethical and legal expectations. Key drivers of the IoT adoption were outlined: interoperability; data privacy and security; flexible governance structures; proper business planning and models.Practical implicationsThe study benefits researchers and industry practitioners alike. For researchers, the identified gaps reveal areas of high priority in future research. For construction companies, particularly small to medium-sized businesses, the study raises awareness of the latest developments and potential applicability of the IoT in the industry. For government agencies and policymakers, this study offers a point of reference in directing the adoption of the IoT smoothly in the construction sector and provides guidelines and standards for maximising the potential benefits.Originality/valueThe study is the first scientometric review of the existing body of knowledge in the context of application of the IoT in the construction industry. Findings expose knowledge gaps in contemporary research, specifically, a broader consideration of organisational adjustments needed to accommodate the IoT usage, economic analyses and impediments to wider acceptance.

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.

Under the background of prosperity of Internet of things (IoT), in order to accelerate the training of talented persons of China for IoT, we propose to design and develop a new course for Chinese students: Essential Technologies, Applications, and Commercial Patterns of Internet of Things. Aiming at three-dimensional target, which has dimensionalities of attitude and thoughts, knowledge and skills, process and methods, the contents of the course include the following parts: system and application architectures, key technologies of IoT, applications of IoT, commercial patterns of IoT, IoT industry and social significance etc. The course highlights the training of abilities of innovation, creativity, comprehensive analysis and practical operations. The construction of teaching resources, such as textbooks, lecture notes, teaching equipments, practice bases, are also emphasized in the process of development of this course.

Communication has become a significant part of today’s world. With the escalating number of advancements in the field of the Internet of Things (IoT) and related notions like cyber-physical systems (CPS), Industry 4.0, and Industrial Internet of Things (IIoT), the need for efficient communication arises. Depending on the distance required for the application and the quality of transmission, there are multiple communication protocols that can be utilized. Various technologies that are used for communication in IoT devices are discussed and compared in this paper. Other related concepts mentioned above are introduced and briefed. Since more and more institutions are starting to adopt technologies from IoT and advanced manufacturing features from IIoT as a part of their systems, these notions are no more an extravagance but are a requirement. The personalization aspect of IoT is what draws businesses and organizations to equip their products with its technologies. This in turn raised questions related to data security. The purpose of this paper is to introduce the notions, compare how various enabling technologies are used to help devices communicate, and discuss the possible attacks in systems involving them.

Internet of Things (IoT) is a concept that proposes the inclusion of physical devices as a new form of communication, connecting them with various information systems. Nowadays, IoT cannot be reduced to smart homes as due to the recent technological advances, this concept has evolved from small to large-scale environments. There was also a need to adopt IoT in several business sectors, such as manufacturing, logistics or transportation in order to converge information technologies and technological operations. Due to this convergence, it was possible to arrive at a new IoT paradigm, called the Industrial Internet of Things (IIOT). However, in IIoT, it is also necessary to analyze and interact with a real system through a virtual production, which refers to the use of Augmented Reality (AR) as a method to achieve this interaction. AR is the overlay of digital content in the real world, and even play an important role in the life cycle of a product, from its design to its support, thus allowing greater flexibility. The adoption of interconnected systems and the use of IoT have motivated the use of Artificial Intelligence (AI) because much of the data coming from several sources is unstructured. Several AI algorithms have been used for decades aiming at “making sense” of unstructured data, and transforming it into relevant information. Therefore, converging IoT, AR, and AI makes systems become increasingly autonomous and problem solving in many scenarios.

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.

A Survey on blockchain for industrial Internet of Things

Although the Internet of Things (IoT) will play a key role in the development of next generation information, network, and communication technologies, many are still unclear about what makes IoT different from similar concepts.Answering fundamental questions about IoT architectures and models, Unit and Ubiquitous Internet of Things introduces essential IoT concepts from the perspectives of mapping and interaction between the physical world and the cyber world. It addresses key issues such as strategy and education, particularly around unit and ubiquitous IoT technologies. Supplying a new perspective on IoT, the book covers emerging trends and presents the latest progress in the field. It also: Outlines a fundamental architecture for future IoT together with the IoT layered model Describes various topological structures, existence forms, and corresponding logical relationships Establishes an IoT technology system based on the knowledge of IoT scientific problems Provides an overview of the core technologies, including basic connotation, development status, and open challenges The book examines ubiquitous sensing, networking, and communications, as well as information management involved in unit IoT. It describes global IoT applications and includes coverage of ubiquitous, local, industrial, national, and transnational IoT. Presenting detailed case studies that illustrate various application scenarios, the text considers the main IoT supporting technologies including resource management, loop control in actuation, session management, space-time consistency, security and privacy, energy management, spectrum management, nanotechnology, quantum technology, and big data.

The proliferation of the Internet of things (IoT) technologies has opened new avenues for technological advancements in various aspects of life. At the same time, the immense collection of competing IoT solutions has created new challenges in security and standards gaps that have attracted numerous research efforts. The majority of these research efforts are centered on the challenges of IoT in general. However, there is a growing separation between the two subcategories of IoT, the Consumer IoT (CIoT) and Industrial IoT (IIoT), in terms of application, growth, scale, requirements, and challenges. The ability to distinguish between the two is critical for developing appropriate solutions, infrastructure, and standardization. This would pave the way to creating reliable, scalable, and secure CIoT and IIoT applications. This study highlights the differences between IoT types in terms of purpose, hardware needs, communication protocol requirements, and security concerns. We also investigate existing IIoT-related solutions to provide readers with clear and concise information to help them make more informed decisions about IoT solutions.

Major advances in the Internet of Things (IoT) technologies are offering unprecedented automated services to various sectors. IoT has shown great promise in transforming buildings and institutions into smart buildings and institutions, making traditional task management more efficient. In particular, there has been remarkable progress in successfully integrating IoT into various contexts, such as smart homes, businesses and cities. Success in such technological transformations is not only due to the proper deployment and implementation of IoT but also to the effective employment of related technologies, such as big data and cloud computing technologies, and making informed decisions on the inclusion of the required hardware and software for all the elements involved in the IoT environment. While a significant amount of IoT research in various contexts has been proliferated in recent years, attention given to its deployment for linguistic landscapes (LLs) is still very rare. The aim of this paper is to propose a smart Al-Noor Mall LL which takes into account the appropriate deployment of IoT tools and relevant technologies. The paper also thoroughly discusses the background of the field, covering IoT technologies, cloud computing, and big data studies, followed by a review of the recent publications involving the successful deployment of IoT technologies combined with big data and cloud computing in various contexts. The literature also covers the success factors that contribute to the robustness of the required real-time operating system (RTOS) in an IoT-based environment in order to enable IoT devices to manage their resources efficiently. Based on a detailed analysis of the literature, a three-level framework (i.e. the object, communications and application levels) together with the elements that are required for IoT-based smart LL solutions is proposed. A case study of Al-Noor Mall is used to validate the proposed framework and the results indicate that IBM Bluemix can help Al-Noor Mall management and customers to locate any particular shop in the huge shopping mall, which thereby makes the customers’ shopping experience more satisfying. Finally, the challenges (i.e. energy efficiency, security, and intelligent data analysis) facing the successful deployment of IoT technologies are discussed with proposals for future research directions in the IoT research field.

Smart city is a city that increases the interactivity of its components and put emphasis on their functionality. Internet of Things technology (IoT) is an innovative solution in environmental protection. Usually, information on air quality is very scattered. This paper describes the test stages of pre-implementation works, focusing on the presentation of the technical design of the measurement nodes and the assumptions of the IT project. The goal of the project Intelligent Wireless Sensor Network Infrastructure (IIBSC) is, among others, to create a dense network of air quality measurement nodes at city, district or even street level. The concept is based on Internet of Things (IoT) technology using a matrix construction tool connected to multiple identical measurement nodes located in the test area. The project developed a hardware platform supporting sensors and resistant to external factors, and an ISIMPIO information platform based on edge processing technology for processing data from air quality sensors. Due to the use of Internet of Things (IoT) technology, an edge server using edge processing was designed. Edge server provides a complete ecosystem for building edge applications that are fully optimized for seamless field work. In addition, it allows the implementation of integrated Python software, the MQ Telemetry Transport support protocol (MQTT), time-series database, firmware update over a wireless network, and built-in security system. Measuring the concentration of particulate matter and other substances in the air will be useful for specialists assessing their dynamics. The technology and test installation selected corresponds to the leading solutions in this field in Europe and, in the future, should also be extended to less urbanised areas.

The ‘Internet of Things’ (IoT) is at times mythologized and its purpose mistaken, and often people can become confused about what it means, does or aims to achieve; moreover, without a financial appraisal of the differences IoT can make to their own enterprise, business leaders may be content to watch and wait rather than to take a lead. This issue of Textile Progress aims to provide a guide to help navigation beyond general statements about IoT and to help those involved with the textile industry to make an informed decision about its potential value to them. The definitions of the Internet of Things (IoT) available in the literature, and those put forth by organizations working on IoT standards development, are reviewed along with its architecture, elements, standards and protocols to help provide an understanding of the concepts and goals of IoT. A broad overview of impediments challenging the progress of IoT, especially in relation to cybersecurity, is provided. This review also compares ongoing work in the application of IoT in the textile industry to that in other manufacturing sectors whilst focussing on the interpretation of IoT technologies and their potential application to the textile industry according to both technological and business perspectives. A specific case study for the spinning industry is conducted to help with evaluation of its IoT solutions and to help to address how other parts of the textile industry might benefit from its application. The case study includes evaluation of IoT solutions in the spinning industry spanning the period between ITMA 2011 to 2019, and takes information from interviews with industry executives to inform future directions of IoT in the spinning industry.

As a typical application of the Internet of Things (IoT), the Industrial IoT (IIoT) connects all the related IoT sensing and actuating devices ubiquitously so that the monitoring and control of numerous industrial systems can be realized. Deep learning, as one viable way to carry out big-data-driven modeling and analysis, could be integrated in IIoT systems to aid the automation and intelligence of IIoT systems. As deep learning requires large computation power, it is commonly deployed in cloud servers. Thus, the data collected by IoT devices must be transmitted to the cloud for training process, contributing to network congestion and affecting the IoT network performance as well as the supported applications. To address this issue, in this article, we leverage the fog/edge computing paradigm and propose an edge computing-based deep learning model, which utilizes edge computing to migrate the deep learning process from cloud servers to edge nodes, reducing data transmission demands in the IIoT network and mitigating network congestion. Since edge nodes have limited computation ability compared to servers, we design a mechanism to optimize the deep learning model so that its requirements for computational power can be reduced. To evaluate our proposed solution, we design a testbed implemented in the Google cloud and deploy the proposed convolutional neural network (CNN) model, utilizing a real-world IIoT data set to evaluate our approach. 1 Our experimental results confirm the effectiveness of our approach, which cannot only reduce the network traffic overhead for IIoT but also maintain the classification accuracy in comparison with several baseline schemes. 1 Certain commercial equipment, instruments, or materials are identified in this article in order to specify the experimental procedure adequately. Such identification is not intended to imply recommendation or endorsement by the National Institute of Standards and Technology, nor is it intended to imply that the materials or equipment identified are necessarily the best available for the purpose.

Internet of Things (IoT) is known as the third wave of the information technology revolution following the computers and Internet. It is believed that IoT industry will drive the next economic growth. IoT technologies, such as radio-frequency identification (RFID), wireless sensing, and nanotechnology, enable the Internet to be expanded to real objects, therefore connecting of the physical world with the Internet's cyber world.

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.