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Industrial Internet of Things (IIoT): Principles, Processes and Protocols
Abstract
The Industrial Internet of Things (IIoT) is a paradigm shift, primarily in the domain of manufacturing industry. The concept is highly attractive for a majority of the industrial sectors due to better operational efficiency capabilities in the production process, smart objects identification mechanisms by embeddedness technologies, intelligent automation abilities and around the clock monitoring abilities. Importantly, it reduces workforce intervention in risky industrial environments. Some of the best practicing places and activities for the IIoT employment are factory shop floors, materials handling, assembly lines, production processes, finalising goods, and other inbound and outbound logistical tasks. The basis for the IIoT phenomenon growth is the Internet of Things (IoT) technologies, which have currently been ensuring efficient work execution in many spheres, industrial as well as commercial and social. This chapter provides a discussion on IIoT concepts and definitions, on business drivers behind the growth of this technology, and the evolution process of this phenomenon. This contribution also discusses the fundamental underlying principles, related technologies, deployment approaches in different areas and associated frameworks. The chapter also explore Japanese Industry-specific case studies, where the industries have already been employing the IIoT-related practices. These include Zenitaka Corporation, Tsuchiya-Gousei, Toyota and Hitachi. This book chapter provides a broader overview in crystal clear and sets the background for the rest of the chapters in this book.
... The present COVID-19 pandemic epidemic makes it abundantly evident how important Smart Health Monitoring and IoTS are. A variety of smart health monitoring models and smart devices significantly contributed to preventing social gatherings and rust at the hospitals two years ago, when the WHO declared covid 19 to be a pandemic and cases were starting to spread [11], [12]. [11] This aided in the treatment of patients suffering from various ailments. ...
... A variety of smart health monitoring models and smart devices significantly contributed to preventing social gatherings and rust at the hospitals two years ago, when the WHO declared covid 19 to be a pandemic and cases were starting to spread [11], [12]. [11] This aided in the treatment of patients suffering from various ailments. In these fields as well as many others, including banking, education, business, and philanthropy, smartphones have proven to be indispensable. ...
... Smart Health Monitoring enables real-time remote monitoring of a covid patient's health. Now that the COVID 19 pandemic is largely under control, people are using teleconsultations and online appointments more frequently than going to hospitals, pharmacies, or community events [11]. Everyone would like to schedule an appointment online or have a teleconsultation now that the pandemic has been effectively contained. ...
... The adoption of IIoT in industrial settings has paved the way for real-time monitoring, data-driven decision-making, and the transformation of traditional industries into intelligent, connected ecosystems by using certain IIoT principles, processes and protocols [28]. The continuous advancements in IIoT technologies, along with the increasing availability of scalable solutions, have further accelerated the adoption and integration of IIoT in industrial environments. ...
... IIoT dashboards saw advancements in terms of user interface design, interactive features, and customizable visualization options. These enhancements improve the user experience, making it easier for operators and decision-makers to understand complex industrial data and take prompt actions [28]. ...
... With the success of the IoT concept, it began to be used in the industrial environment, where it is called the Industrial Internet of Things (IIoT). The IIoT is a subset of the IoT, which consists of sensor networks (industrial fieldbuses), actuators, robots, machines, appliances, business processes, and personnel [7,8] in order to achieve an efficient and intelligent manufacturing process. With the development of the fog-computing concept for IoT, it has also started to be used for IIoT [8]. ...
... The result of the communication operations on the CANOpen fieldbus is sent to the buffer memory (see (6) from Figure 3). Data that is transmitted on the fieldbus can be retrieved from the buffer memory (see (7) from Figure 3). The buffer memory is protected by a semaphore to achieve mutual exclusion when it is accessed. ...
In the design and development process of fog computing solutions for the Industrial Internet of Things (IIoT), we need to take into consideration the characteristics of the industrial environment that must be met. These include low latency, predictability, response time, and operating with hard real-time compiling. A starting point may be the reference fog architecture released by the OpenFog Consortium (now part of the Industrial Internet Consortium), but it has a high abstraction level and does not define how to integrate the fieldbuses and devices into the fog system. Therefore, the biggest challenges in the design and implementation of fog solutions for IIoT is the diversity of fieldbuses and devices used in the industrial field and ensuring compliance with all constraints in terms of real-time compiling, low latency, and predictability. Thus, this paper proposes a solution for a fog node that addresses these issues and integrates industrial fieldbuses. For practical implementation, there are specialized systems on chips (SoCs) that provides support for real-time communication with the fieldbuses through specialized coprocessors and peripherals. In this paper, we describe the implementation of the fog node on a system based on Xilinx Zynq UltraScale+ MPSoC ZU3EG A484 SoC.
... Experiments have shown that the accuracy reaches 92% in classifying suitable postures and sending notifications to the employee's PC to remind them to change their lying position when necessary. Madakam and Uchiya [9] presented the basic principles, related technologies, and methods of IoT implementation in different industrial fields and emphasized the importance of related technology platform methods. The work relates the implementation of IIoT in Japan with industrial corporations such as Zenitaka Corporation, Tsuchiya-Gousei, Toyota, and Hitachi. ...
Applying IoT systems in industrial production allows data collection directly from production lines and factories. These data are aggregated, analyzed, and converted into reports to support manufacturers. Business managers can quickly and easily grasp the situation, making timely and effective management decisions. In industrial sewing, IoT applications collect production data from sewing lines, especially from industrial sewing machines, and transmit that data to cloud-based systems. This allows businesses to analyze production situations, thereby improving management capacity. This article explores the implementation of IoT applications at industrial sewing enterprises, focusing on data collection during the production process and proposing a data structure to integrate this information into the company’s MIS system enterprise. In addition, the research also considers applying the Real-RCPSP problem to support businesses in planning automatic production operations.
... Later, the growing implications and usage of IoT for industrial processes led to the emergence of the industrial/manufacturing/operational stream of IoT researchcommonly known as the Industrial Internet of Things (IIOT) (Boyes et al., 2018;Sisinni et al., 2018;Madakam and Uchiya, 2019)which focused on topics such as smart production processes (e.g., Zhang et al., 2018), intelligent automation and assembly (e.g., Liu et al., 2017), industrial safety (e.g., Gnoni et al., 2020;McNinch et al., 2019), and such. The emphasis here is on the role of IoT in improving operational processes in industrial spaces. ...
Purpose
This paper systematically reviews the evolution of Internet of Things (IoT) research in business and management over the past decade and a half. It synthesizes current knowledge, identifies major themes, gaps, and future opportunities to guide scholars on potential research directions within this exponentially growing domain.
Design/methodology/approach
A structured systematic literature review methodology filtered IoT publications across business/management journals using Scopus database. Detailed thematic and bibliometric analyses chronologically mapped the progress of peer-reviewed articles from 2005–2023. Both quantitative metrics and qualitative coding inductively revealed historical trends, topics, applications and research implications.
Findings
Analysis uncovered six primary IoT research themes - business models, technology, data, customers, organizations, and sustainability. Dominant focuses were found on technological enablers, business model innovation and customer experience transformations. While technical aspects are well-documented, strategic technology integrations and organizational change management require greater emphasis.
Research limitations/implications
Focus restricted to academic articles published in management journals risks missing relevant papers published in other fields. Screening process involved some subjectivity. Lacks geographic analysis of research contexts. The rapidly evolving nature of technology domain risks findings’ generalizability.
Practical implications
Key enablers and success factors that we identified may support managerial decision making when it comes to IoT adoption.
Social implications
We discuss advancing IoT innovation through ethics and sustainability lenses and these may help ensure responsible adoption.
Originality/value
This analysis weaves together the extant literature and offers an evidence-based research agenda for management scholars by chronicling the state, evolution, influential factors, and future opportunities within IoT literature. It highlights major thematic shifts and priority gaps to address.
... The IoT enables the connectivity of devices, sensors, and systems, facilitating real-time data collection and analysis. It allows for seamless communication between various components of industrial processes, enabling enhanced monitoring, control, and safety measures (Madakam and Uchiya, 2019;Javaid et al., 2021). Industry 4.0 principles, including cyber-physical systems and data-driven decision-making, can optimize process safety by enabling better situational awareness and risk management (Junior et al., 2018;Amin et al., 2022). ...
This review explores the evolving landscape of process safety, emphasizing the integration of digitalization and advanced technologies. It assesses the role of the industrial Internet of Things, artificial intelligence, and machine learning (ML) in enhancing safety protocols. The paper examines predictive analytics, sensor technology advancements, and digital twins' contributions to safety optimization. It also discusses the future perspectives of risk management approaches, including proactive safety management systems, quantitative risk assessment techniques, and human reliability analysis. This comprehensive analysis provides insights into future developments and challenges in process safety.
... With the introduction of the concept of the IoT into industrial applications [13], the concept of IIoT was defined. IIoT is a subset of IoT, and it consists of fieldbuses, sensors, actuators, robots, machines, appliances, business processes, and personnel [14,15]. IIoT has significant market potential, companies studying the market estimate that it will reach a market value of USD 106.1 billion in 2026, with an annual average growth of 6.7% from USD 88.2 billion in 2023 [16]. ...
The use of the Internet of Things (IoT) technologies and principles in industrial environments is known as the Industrial Internet of Things (IIoT). The IIoT concept aims to integrate various industrial devices, sensors, and actuators for collection, storage, monitoring, and process automation. Due to the complexity of IIoT environments, there is no one-size-fits-all solution. The main challenges in developing an IIoT solution are represented by the diversity of sensors and devices, connectivity, edge/fog computing, and security. This paper proposes a distributed and customized IioT (Industrial Internet of Things) framework for the interaction of things from the industrial environment. This framework is distributed on the fog nodes of the IIoT architecture proposed, and it will have the possibility to interconnect local things (with low latency) or global things (with a latency generated by the Internet network). To demonstrate the functionality of the proposed framework, it is included in the fog nodes presented in other paper. These fog nodes allow the integration of CANOpen networks into an IioT architecture. The most important advantages of the proposed architecture are its customizability and the fact that it allows decision operations to be carried out at the edge of the network to eliminate latency due to the Internet.
... Today smart factories have opened the road for a new industrial transformation known as industry 4.0. By adopting the industry 4.0 standard, industrial machines can interact with each other and automatically operated with little or no human intervention to enable real-time production [1], [2]. The main objective of an IIoT system is to assure the efficacy of industrial systems operations, improve productivity, and enhance the control of industrial systems [3]. ...
Incorporating internet of things (IoT) in industrial systems prompted the
development of industrial internet of things (IIoT) systems, which in turn
enable the automation of intelligent devices to gather, analyze, and transmit
data from industrial systems in real-time. This paper develops a low-cost and
smart industrial remote monitoring and control system based on NodeMCU
microcontrollers and Blynk server platform. It is deployed to remotely
monitor manufacturer's environment and industrial equipment and control
them autonomously. Also, it protects the manufacturer's employees from fire
catastrophe by warning them using a buzzer and notification. The system
comprises two main parts, sensing and actuation. The sensing part consists of
three subsystems that measure temperature and humidity, water flow, and
flame. The actuation part consists of a water pump, light, and fan. A powerful
user interface is developed based on the Blynk platform. The proposed system
controls the water pump by sensing water flow autonomously. In addition,
based on a fire detected, a protection system is implemented to shut down the
electricity from load in case of fire event occurs. Several testing scenarios
were carried on to check the response of the system, and the result shows
successful implementation of the proposal to handle different situations
... Internet of Things industri menghubungkan mesin dan perangkat di pabrik dan lokasi industri lainnya, tujuan tersebut untuk menjaga peralatan tetap beroperasi. Perusahaan menggunakan teknologi IoT untuk mengotomatiskan proses yang sebelumnya manual dan mengelola aset dari jarak jauh, sehingga dapat menghasilkan efisiensi baru dan penghematan biaya (Madakam and Uchiya, 2019). ...
Internet of Things (IoT) adalah sebuah konsep yang menggambarkan
jaringan yang terhubung antara perangkat fisik dan perangkat digital
dengan menggunakan teknologi komunikasi. Perangkat fisik tersebut
dapat berupa sensor, peralatan rumah tangga, mobil, atau bahkan
bangunan. Perangkat digital yang terhubung dengan perangkat fisik
tersebut dapat berupa komputer, smartphone, atau tablet.
IoT memungkinkan perangkat fisik untuk terhubung ke internet dan
saling berkomunikasi dengan perangkat digital lainnya. Dengan adanya
IoT, kita dapat mengontrol dan memantau perangkat fisik dari jarak jauh,
mengambil data dari perangkat fisik, dan bahkan memberikan instruksi
kepada perangkat fisik melalui perangkat digital.
Buku "Internet of Things (IoT): Teori dan Implementasi" akan membahas
tentang :
Bab 1 Pengantar Internet Of Things (IoT)
Bab 2 Sejarah Internet Of Things (IoT)
Bab 3 Konsep Dasar Internet Of Things (IoT)
Bab 4 Arsitektur Internet Of Things
Bab 5 Standarisasi Internet Of Things (IoT)
Bab 6 Evolusi Internet Of Things
Bab 7 IoT Hari Ini
Bab 8 Aplikasi Internet Of Things (IoT)
Bab 9 Perkembangan Internet Of Things (IoT)
Bab 10 Masa Depan Internet Of Things (IoT)
Bab 11 Penelitian Tentang Internet Of Things
Bab 12 Privasi Dan Keamanan Internet Of Things (IoT)
Bab 13 Implementasi Internet Of Things (IoT) Pada Kota Pintar
Bab 14 Implementasi Internet Of Things (IoT) Pada Bidang Pendidikan
vi Internet of Things (IoT): Teori dan Implementasi
Bab 15 Implementasi Internet Of Things (IoT) Pada Bidang Kesehatan
Bab 16 Implementasi Internet Of Things (IoT) Pada Bidang Industri
Dengan membaca buku ini, Anda akan memahami konsep IoT secara
mendalam, serta dapat membuat sebuah sistem IoT sesuai dengan
kebutuhan Anda. Selain itu, Anda juga akan mengetahui aplikasi IoT dari
berbagai bidang, sehingga dapat membantu Anda dalam mengembangkan
ide-ide baru yang berkaitan dengan IoT.
... In order to address modern technological challenges in this industry 4.0 era, the construction industry must adapt and transform from primitive methods to digitalized automated processes as a critical step toward increasing productivity, efficiency, and environmental sustainability, as well as dynamic management and planning (Maqbool et al. 2022a;Dallasega 2018). The Internet of Things is expected to significantly impact the construction sector financially by ensuring high reporting speeds and lowering communication costs (Ramasundara et al. 2018) and by improving process control and optimization (Madakam and Uchiya 2019). Even at the micro level, the massive volume of big data collected will improve monitoring and analysis, increase accountability and transparency, and highlight the adequacy of key performance indicators (KPIs). ...
Future construction projects will need the implementation of industry 4.0 and Internet-of-Things (IoT) technologies. The construction sector has, however, falling behind other industries in the application of these technologies and is currently facing considerable challenges. One of the industries that lag behind in the use of new innovative technological tools is the construction industry. This study reviews the research work in industry 4.0 and the Internet of Things as they relate to construction and examines key Ghana-based construction professionals and firms to ascertain their level of understanding of these emerging innovative technologies, including the challenges and benefits associated with their implementation. An extensive review of pertinent literature was done to help identify the important paradigms and variables which were cautiously tested. Adopting a quantitative research approach, the attained variables were used to design into a close-ended questionnaire. The sample frame was a survey of people from 154 construction experts and researchers with good standing by using the purposive sampling. Relative importance index (RII) analysis was used to analyzed the data. It was discovered from the findings that smart construction was the most popular industry 4.0 technology in the Ghanaian construction industry. The most important benefit of these technologies is that they will add sustainable policy requirements to tendering, with the most pressing technology being the lack of talent and skills in using industry 4.0 and IoT technologies. The scope of this research is based on the questionnaire survey, proving a sustainable pathway to the construction industry community, which creates its own significance by including key stakeholders and those affected by these technologies.
... Challenges of the IIoT include, but are not limited to, energy efficiency, interoperability, and security [13][14][15][16][17][18][19][20]. The sensor technology is a basic element of the IoT and IIoT. ...
A lithium-ion battery is a type of rechargeable battery which is widely used in many applications, such as electronic products and electric vehicles. Practical applications use many lithium-ion batteries which are connected in series and in parallel. Many incidents have occurred due to battery safety issues in recent years. The connection of lithium-ion batteries has safety problems if the batteries are not well controlled in packing and assembling. In this regard, the use of enabling technologies is essential to the proper operation of the batteries’ assembly manufacturing. This paper investigates the manufacturing of lithium-ion batteries smartly controlled by the industrial internet of things (IIoT)-based configuration for a real case. The paper further describes the implementation and its evaluation using various sensor nodes and subsystems. An event data model and a solution present the ability to enable data sharing and interoperability among various components in our work. The solution has highlighted a number of key capabilities and aspects of novelty in lithium-ion battery manufacturing and the IIoT.
... Journals were qualified for investigation based on the number of papers released relevant to the keyword "Industry 4.0". Twelve top-tier journals across Architecture, Engineering and Construction Management were selected as a basis for exploration (Madakam and Uchiya, 2019). ...
Purpose
This study aims to provide an excellent overview of current research trends in the construction sector in digital advancements. It provides a roadmap to policymakers for the effective utilisation of emergent digital technologies and a need for a managerial shift for its smooth adoption.
Design/methodology/approach
A total of 3,046 peer-reviewed journal review articles covering Internet of Things (IoT), blockchain, building information modelling (BIM) and digital technologies within the construction sector were reviewed using scientometric mapping and weighted mind-map analysis techniques.
Findings
Prominent research clusters identified were: practice-factor-strategy, system, sustainability, BIM and construction worker safety. Leading countries, authors, institutions and their collaborative networks were identified with the UK, the USA, China and Australia leading this field of research. A conceptual framework for an IoT-based concrete lifecycle quality control system is provided.
Originality/value
The study traces the origins of the initial application of Industry 4.0 concepts in the construction field and reviews available literature from 1983 to 2021. It raises awareness of the latest developments and potential landscape realignment of the construction industry through digital technologies conceptual framework for an IoT-based concrete lifecycle quality control system is provided.
... The cloud server feeds back the analysis results to the device through big data analysis technology. This model can optimize industrial production management and improve industrial production efficiency [16]. In this process, a large amount of private data collected by wireless sensors will be exposed on the Internet, which is easy to be stolen and attacked by hackers [17]. ...
With the development of the Industrial Internet of Things (IIoT), industrial wireless sensors need to upload the collected private data to the cloud servers, resulting in a large amount of private data being exposed on the Internet. Private data are vulnerable to hacking. Many complex wireless-sensor-authentication protocols have been proposed. In this paper, we proposed an efficient authentication protocol for IIoT-oriented wireless sensor networks. The protocol introduces the PUF chip, and uses the Bloom filter to save and query the challenge-response pairs generated by the PUF chip. It ensures the security of the physical layer of the device and reduces the computing cost and communication cost of the wireless sensor side. The protocol introduces a pre-authentication mechanism to achieve continuous authentication between the gateway and the cloud server. The overall computational cost of the protocol is reduced. Formal security analysis and informal security analysis proved that our proposed protocol has more security features. We implemented various security primitives using the MIRACL cryptographic library and GMP large number library. Our proposed protocol was compared in-depth with related work. Detailed experiments show that our proposed protocol significantly reduces the computational cost and communication cost on the wireless sensor side and the overall computational cost of the protocol.
... The way that IoT enhances operational efficiency is one of the numerous benefits of manufacturing. Linked sensors can recognise the probable failure and activate an engineer's repair request [79,80]. Manufacturers use IoT to incorporate vibrant, competent, and automated production operations, in which maintenance schedules are independent. ...
Now a day's manufacturing has become more intelligent and data-driven. A smart production unit can be thought
of as a powerful connected industrial system with materials, parts, equipment, tools, inventory, and logistics that
can transmit data and communicate with one another in the age of the Industrial Internet of Things (IIoT). This
IIoT refers to linked devices, sensors, and other equipment that may be networked in the industrial environment
to provide remote access, effective monitoring, better data collecting, analysis, & exchange etc. In Industry 4.0,
IIoT is fundamental to transforming cyber-physical systems and production processes through big data and analytics.
This paper provides an overview of the IIoT and the technologies that underpin it. The primary benefits
and features of IIoT in manufacturing are discussed in detail. Smart Transformations made into the manufacturing
field through IIoT Culture are discussed diagrammatically. Finally, twenty-nine significant applications of IIoT in
the field of manufacturing are identified and discussed. IIoT can monitor the transport, supply of the goods,
consult details on things in warehouses, and check the conditions related to product storage and delivery and
allow all dispersed and outsourced operations to be monitored. Therefore, the industry is being revolutionised by
IIoT, altering the way industrial enterprises function daily to improve efficiency and performance levels.
... It reduces employee intervention in hazardous industrial circles. e IIoT phenomenon is built on the IoT (Internet of ings) technologies, which presently guarantee effective working performance in many domains, both in the sector and business and commercial sectors [15]. Enhanced sensing, data collecting, and communication technologies lead to an enormous expansion of the IIoT in recent years, which intensifies the transformation in the monitoring and management of electronic asset ...
Internet of Things (IoT) is a computing term which describes universal Internet connectivity, transforming everyday objects into connected devices. Many smart devices are interconnected to sense their surroundings, send, and process the sensed data. The IoT connects the real world with the global world by interconnecting edge devices. The main goal of the IoT is to attain high operating performance, improve throughput, and control the assets and processes of the industry. Many heterogeneous devices in IoT settings are linked with each other to transfer huge amount of information for operations of organizational efficiency. The appropriate and proper device may hinder the main goals of the IoT which seems difficult to achieve. However, not a single research study is focused on the selection of devices based on multicriteria properties. For solving the dilemma of the IoT device selection, “Properties Based Device Selection Using Ant Colony Optimization (PBDS-ACO)” is implemented in this paper which selects a device based on multicriteria properties. By exploiting the suggested model, the effectiveness and efficiency of the IoT are shown.
... is included Tsuchiya Gousei, Toyota, Hitachi, and the Zenitaka Corporation [44]. Connectivity is a one-word definition of Industry 4.0 revolution. ...
Internet of Things (IoT) has been considered as one of the emerging network and information technologies that can comprehend automatic monitoring, identification, and management through a network of smart IoT devices. The effective use of IoT in different areas has improved efficiency and reduced errors. The rapid growth of smart devices such as actuators, sensors, and wearable devices has made the IoT enable for smart and sustainable developments in the area. Physical objects are interlinked with these smart devices for the progression to analyse, process, and manage the surroundings data. Such data can then be further utilised for smarter decisions and postanalysis for different purposes. However, with the limited IoT resources, the management of data is difficult due to the restrictions of transmission power place and energy consumption, and the processing can put pressure on these smart devices. The network of IoT is connected with big data through Internet for manipulating and storing huge bulk of data on cloud storage. The secure framework based on big data through IoT is the awful need of modern society which can be energy efficient in a sustainable environment. Due to the intrinsic characteristics of sensors nodes in the IoT, like data redundancy, constrained energy, computing capabilities, and limited communication range, the issues of data loss are becoming among the main issues which mostly depend on the completeness of data. Various approaches are in practice for the recovery problem of data, such as spatiotemporal correlation and interpolation. These are used for data correlation and characteristics of sensory data. Extracting correlation data became difficult specifically as the coupling degree between diverse perceptual attributes is low. The current study has presented a comprehensive overview on big data and its V's with Internet of Things to describe the research into the area with in-depth review of existing literature.
... It is predicted that the IoT will have a financially beneficial and significant impact upon the sector by ensuring high speed of reporting to reduce the costs of communication (Ramasundara et al., 2018) and providing better process control and optimisation (Madakam and Uchiya, 2019). The huge amount of big data collected will improve monitoring and analysis even at the micro level to provide better accountability and transparency, as well as highlight the key performance indicators (KPIs) and their adequate monitoring. ...
Purpose
The 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/approach
An 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.
Findings
The 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 implications
The 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/value
The 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.
Objective
The development of key technologies for the Industrial Internet is a major concern for countries worldwide. This paper aims to comprehensively understand the technology of the Industrial Internet by analyzing its current application status and trends. It will dynamically examine the key technologies and development trends of the Industrial Internet, providing a valuable reference for technological advancements in this field.
Methods
This paper analyzed global patent data in the field of the Industrial Internet from 1965 to 2023. The paper applied the BERTopic model and the all-MiniLM-L6-v2 model to extract and vectorize topics related to industrial internet technology from patent texts. Based on the theory of Internet governance, the paper categorizes the topics into four categories. The paper then established the Hidden Markov Model (HMM) to investigate the evolutionary mechanism of technological topics. The paper utilized the newly divided topics as hidden states and the number of patent applications as observed states in the Hidden Markov Model (HMM).
Results
Industrial internet technology encompasses five research directions. The physical layer focuses on interconnection platforms for equipment, as well as devices for the storage and monitoring of liquids and gases. The logical layer involves remote control systems for industrial equipment, while the data layer focuses on data processing and information services. The interaction layer included modular image processing and control methods. Among these types of technologies, the data layer technologies were the most developed and also contributed to the advancement of interaction layer technologies. The physical layer technologies were relatively more developed, while the logical and interaction layer technologies were relatively less developed.
This paper proposes an embedded Internet of Things (IoT) system for bioreactor sensor integration, aimed at optimizing temperature and turbidity control during cell cultivation. Utilizing an ESP32 development board, the system makes advances on previous iterations by incorporating superior analog-to-digital conversion capabilities, dual-core processing, and integrated Wi-Fi and Bluetooth connectivity. The key components include a DS18B20 digital temperature sensor, a TS-300B turbidity sensor, and a Peltier module for temperature regulation. Through real-time monitoring and data transmission to cloud platforms, the system facilitates advanced process control and optimization. The experimental results on yeast cultures demonstrate the system’s effectiveness at maintaining optimal growth, highlighting its potential to enhance bioprocessing techniques. The proposed solution underscores the practical applications of the IoT in bioreactor environments, offering insights into the improved efficiency and reliability of culture cultivation processes.
Permeability of shell molds for investment castings used in high-valu segments including aerospace, biomedical and defense plays a very important role in achieving better quality castings especially free from filling-related defects. Though, guidelines related to permeability measurement techniques have been established for twenty years, the permeability measurement device is not available in most investment casting foundries. This can lead to issues when establishing foundries to supply investment castings for high-value segments. A specific permeability measurement device has been developed by following guidelines published by a globally accepted standard body that further has been facilitated with the fundamentals of the Internet of Things. Different mold materials (using zirconium sand, aluminum silicate and fused silica) that are used in shell mold making for high-valued segments have been tested for establishing benchmark values of permeability. These values of permeability were observed in the range of 8 × 10-11–37 × 10-11 cm2 to achieve better quality shells as well as investment castings.
Industrial Internet of Things (IIoT) is an integral part of cyber-physical production systems and processes, characterizing the use of modern and disruptive technologies (big data, IoT, and artificial intelligence (AI) among others) to produce consumer goods, considering real-time data from sensors and other sources of information assisting industrial devices and infrastructure in their decision-making, and it has the potential for digital transformation by adding technologies such as analytics. Intelligent Factory is a broad concept developed in Industry 4.0 involving factories with fully integrated cyber-physical systems, through automation, IIoT, high-end industrial machines, Big Data, and AI are examples of technologies included in the implementation of this concept, going beyond the capacity of these machines to work without any human operator in charge, created from the perception of a series of technological advances, allowing intelligent robots and machines to perform increasingly complex functions, it not only affect industries but the entire marketplace. IIoT, in this sense, can be considered a move toward “intelligent machines,” which allows machines to autonomously monitor and predict potential problems, creating operational efficiencies, in which the levels of precision of the operations involved in the respective systems are raised to a level that cannot be achieved through human interventions. Therefore, this manuscript aims to offer an up-to-date overview of the IIoT and your adjacent technologies presenting the principles of this technology. In that regard, it demonstrates a landscape view of the applied aspect, with a concise bibliographic background to point out key concerns and challenges, featuring the potential of technologies.
Small and Medium Enterprises (SMEs) are steadily moving in the direction of implementing digital and smart technologies, including the Industrial Internet of Things (IIoT) for improving their products and services. The adoption of IIoT allows manufactures and producers to make quick decisions for improving productivity and quality in real‐time. For this purpose, the era of digital industrial revolution from IR 1.0 to IR 5.0 is briefly explained. In this research study, the authors have reviewed and analysed the existing reviews, surveys and technical research studies on IIoT technologies for the manufacturing and production SMEs to highlight the concern raised. Forty‐seven (47) influencing factors are identified and classified into four groups based on the TOEI framework. Based on the identified influencing factors, IIoT adoption model is proposed for the manufacturing and production SMEs to adopt the new IIoT technologies in their business environments. Furthermore, a comparative analysis of the influencing factors has been done for the adoption of IIoT to increase efficiency, productivity and competitiveness for the manufacturing and production SMEs in developing countries. The proposed IIoT adoption model will help future policymakers and stakeholders to develop policies and strategies for the successful adoption and implementation of IIoT in manufacturing and production SMEs in developing countries. Also, recommendations are suggested to encourage IIoT adoption in production and manufacturing environments so that manufacturers and producers can respond easily and quickly to highly changing demands, product trends, skills gaps and other unexpected challenges in the future.
Проаналізовано основні концепції та технології автоматизації процесів із застосуванням роботизації (англ. Robotic Process Automatization, RPA) та визначено перспективи застосування RPA у системах інтернету речей (англ. Internet of Things, IoT). Встановлено, що стрімкий розвиток інтернету речей потребує чітких протоколів та підходів для виконання частини процесів в автоматичному режимі з мінімізацією залучення до цього людини. Проаналізовано основні процеси типових систем інтернету речей, встановлено відповідність цих процесів та архітектурних рівнів IoT систем. Визначено, що роботизована автоматизація процесів дає змогу істотно пришвидшити імплементацію рішень для автоматичного управління процесами збирання, управління та оброблення даних у системах інтернету речей. З'ясовано, що окремої уваги вимагає вирішення проблеми автоматизації процесів конфігурації пристроїв та оновлення програмного забезпечення, запуску діагностичних процедур та моніторингу пристроїв інтернету речей, оскільки саме від коректної роботи фізичних пристроїв ІоТ залежить належне функціонування цілої системи. Проаналізовано основні завдання, які можна вирішити за допомогою роботизованої автоматизації процесів у системах інтернету речей та визначено такі основні напрями як: збір та оброблення даних, моніторинг та управління обладнанням, автоматизація рутинних операцій, оптимізація енергоспоживання, автоматизоване оброблення відхилень та помилок, інтеграція та обмін даними, аналітика та звітність, забезпечення безпеки та контролю мережі інтернету речей. Наведено приклади використання RPA в системах інтернету речей, які охоплюють такі сфери, як бізнес аналітика, управління ІоТ пристроями та підтримка програмних продуктів. Здійснено аналіз основних переваг і недоліків використання роботизованої автоматизації процесів в ІоТ та встановлено, що основними перевагами використання засобів RPA є виконання процесів у режимі реального часу та покращення керованості процесами, а основними недоліками – необхідність використання стандартизованих процесів та інтерфейсів та стабільне з'єднання з мережею, що є частими проблемами систем ІоТ. Визначено та окреслено подальші дослідження з автоматизації розгортання інфраструктури, попереднього оброблення даних від кінцевих пристроїв на рівні капілярної мережі та тестування інфраструктури в промисловому інтернеті речей.
Purpose
Sustainable management has become a strategic and popular requirement for stakeholders, as the ability to create competitive advantages in the current dynamic business environment and the pressures of digital transformation need to be increased attention in achieving the highest level of sustainable performance. The main objective of this paper is to explore the direct effects of the industrial Internet of things and supply chain visibility on sustainable performance (economic performance, environmental performance and social performance), in addition to evaluating the indirect effects that supply chain visibility plays in these relationships.
Design/methodology/approach
To achieve the purposes and objectives of this work, a conceptual model was developed by referring to the current works in the literature, and the theoretical model was developed, and then data were collected through the questionnaire and by the snowball sampling method, as 380 questionnaires were used. In the data analysis, the multivariate analysis technique was used via Covariance-based Structural Equation Modeling (CB-SEM), as hypothesized relationships were evaluated through confirmatory factor analysis (CFA), and then the hypotheses were tested through a structural model.
Findings
The results reached the acceptance of all hypotheses, and this means that all hypothesized relationships were positive, as the impact of the industrial Internet of things was positive on each of the supply chain visibility and economic performance, environmental performance and social performance, and also the supply chain visibility mediated the relationship between each of the industrial Internet of things and economic performance, environmental performance and social performance.
Originality/value
The current paper reported a range of theoretical and practical contributions, as this study is the first to examine these hypothesized relationships together in this way and thus helped this study to develop new perceptions about Industry 4.0 technologies and their potential impacts on supply chain and sustainable performance. Also, this study had a variety of management recommendations that allow managers of pharmaceutical manufacturing companies operating in Jordan to develop their technological capabilities and use them to enhance sustainable performance.
Water management is crucial for agriculture, as it is the primary source of irrigation for crops. Effective water management can help farmers to improve crop yields, reduce water waste, and increase resilience to drought. This can include practices such as precision irrigation, using sensors and technology to deliver water only where and when it is needed, and conservation tillage, which helps to reduce evaporation and retain moisture in the soil. Additionally, farmers can implement water-saving techniques such as crop selection, crop rotation, and soil conservation to reduce their water use. Thus, studies aimed at saving the use of water in the irrigation process have increased over the years. This research suggests using advanced technologies such as IoT and AI to manage irrigation in a way that maximizes crop yield while minimizing water consumption, in line with Agriculture 4.0 principles. Using sensors in controlled environments, data on plant growth was quickly collected. Thanks to the analysis and training of these data between several models among them, we find the K-Nearest Neighbors (KNN), Support Vector Machine (SVM) and Naive Bayes (NB), the KNN has shown interesting results with 98.4 accuracy rate and 0.016 root mean squared error (RMSE).
The chapter deals with messages notifying errors in technological equipment in industrial production through the integration of IIoT equipment into the existing cloud infrastructure of an enterprise. At present, management of production is negatively impacted by the human factor. Through integration of the solution proposed, this impact is eliminated and the predictive capacity prevents premature and undesirable need for maintaining the technological equipment. The original messaging used to be done only in the form of a visual notification appearing on the control panel, addressed to the operators present, based on the software visualization platform Wonderware System Platform 2017. The new solution will consist of an original design, which will be supplemented with email and SMS notification using an IIoT device, thus making possible communication between the existing software platform and a GSM modem with a SIM card. The innovativeness of the solution also lies in messages being sent to several responsible persons in the production process rather than just one.
In recent years IoT technology has become widely accepted in the industrial sector creating the fourth industrial revolution, Industry 4.0, and Industrial Internet of Things (IIoT). This wide adoption of IoT has transformed the industries to smarter, efficient, and profitable ones. However with this adoption industry has inherited the vulnerabilities, challenges, and other issues faced by IoT. This paper conveys the challenges and issues created in this transformation and provides various security mechanisms to address the issues faced by the IoT and IIoT domain. The final section opens up various research areas in security that are still to be addressed in these domains and are also discussed in this paper.
The revolution in industrial operations and productions triggered by IoT is like a newborn child that has a lot to be taken care of till reaching its maturity and being able to act independently. The Industrial Internet of Things (IIoT) is a model transformation, in the domain of the manufacturing industry. The industries whether limited to small businesses or large, are in need of machine learning approaches to overcome difficulties created by the data accumulated through web applications, management systems, sensors, and actuators. Leading Data scientists to face the dilemma of choosing machine learning approaches that suit their needs and expectations. As AI-enabled frameworks normally need secure and private cloud services to process data accumulated in the industry, it causes some difficulties like latency, energy consumption, reliability, etc. Thus, integrating AI-enabled approaches into the fog and edge computing is a critical approach to overcome the obstacles and achieve intelligent IIoT. In this chapter, we try to cover challenges faced in IIoT then present recently developed ML-based solutions. After that, we talk about how ML helps us achieve automation in decision making. Finally, the important role of FPGAs to implement ML solutions is discussed.
The Fourth Industrial Revolution (Industry 4.0, I4.0) is the current move in the manufacturing industry to adopt and integrate digital technologies throughout entire company structures with the focus on data and interconnectivity. Although the benefits of these digital technologies are yet to be fully realised, large industrial manufacturing companies are investing heavily into the design and acquisition of digital infrastructure. Small/medium enterprises (SMEs) are set to benefit from this industrial revolution through increased productivity, competitiveness and integration along the supply chain. Barriers for SMEs to adopt the digital technology include the large financial investments in systems and new ‘smart’ equipment to replace non-connected legacy equipment as well as the skills gap of the workforce to develop, maintain and benefit from the technology. To help overcome these barriers, this study compares three solutions for connecting legacy manufacturing equipment to cloud platforms of varying cost, from hundreds to several thousands of pounds, and capability whilst analysing the equipment and skills required to implement the solutions. This paper shows a range of possible solutions available without large scale financial investment, whilst critically assessing the functionality, security, knowledge required and cost for manufacturers. The solutions are implemented and tested on manufacturing systems at the University of Manchester and wider industrial settings originally lacking in connectivity.
One of the biggest difficulties for a visually impaired person is to identify people and read text. This limits the visually impaired to interact socially and puts them at risk. In recent years, several deep learning techniques have been used for face and text recognition. This paper proposes a portable system for face and text recognition using the Raspberry Pi 3B+, Raspberry Pi camera module, customized dataset, few push button switches, and earphones. Multi-task cascaded convolutional neural network and support vector machine are used for face detection and face recognition, respectively. The proposed idea involves efficient and accurate scene text detector for text detection and Tesseract optical character recognition (OCR) for text recognition. Finally, converting the text or face labels to speech by e-Speak tool, a process which allows visually impaired people hear the name of the person/text-written in front of them.
Increasing cost and demand of energy has led many organizations to find smart ways for monitoring, controlling and saving energy. A smart Energy Management System (EMS) can contribute towards cutting the costs while still meeting energy demand. The emerging technologies of Internet of Things (IoT) and Big Data can be utilized to better manage energy consumption in residential, commercial, and industrial sectors. This paper presents an Energy Management System (EMS) for smart homes. In this system, each home device is interfaced with a data acquisition module that is an IoT object with a unique IP address resulting in a large mesh wireless network of devices. The data acquisition System on Chip (SoC) module collects energy consumption data from each device of each smart home and transmits the data to a centralized server for further processing and analysis. This information from all residential areas accumulates in the utility’s server as Big Data. The proposed EMS utilizes off-the-shelf Business Intelligence (BI) and Big Data analytics software packages to better manage energy consumption and to meet consumer demand. Since air conditioning contributes to 60% of electricity consumption in Arab Gulf countries, HVAC (Heating, Ventilation and Air Conditioning) Units have been taken as a case study to validate the proposed system. A prototype was built and tested in the lab to mimic small residential area HVAC systems1.
By releasing of azo dye through textile effluent, textile industry is the main cause of water pollution resulting into acute effect on environment and human health. Development of any eco-friendly and cost-effective method that may address the drawbacks to physical or chemical methods of dye removal is the recent global priority. Physical or chemical methods for textile wastewater pretreatment are of high cost, extremely energy consuming, and environmentally low efficient and generate toxic sludge. Thus, the use of microbial technique for textile dye degradation will be eco-friendly and is probably a lucrative alternative to physico-chemical processes. Microbial enzymes, viz. laccase and azoreductase, are cost-efficient, easy to harvest, easily downstream processable, and effortlessly mobilizable. Recent research trends on nanoparticle-microbial enzyme conjugates are also highly efficient to remove the azo dye from textile waste within a few minutes. But unfortunately, due to some gap between academia and industry, these methods remain only limited up to laboratory and its industrialization is still a challenge. The present review is an illustrated compilation of the use of microbial enzymes in removal of textile dyes.
Global Supply Chain Management is the management of supply chains located globally. Quality control methods such as Lean and Six Sigma are used for improving efficiency and quality of production and distribution carried out by global supply chains. Lean Six Sigma eliminates unnecessary processes and defects which can benefit an enterprise from reduced costs and less wastage of resources. Industry 4.0 refers to automation of industries by the exchange of data between the supply chain and logistics. Industrial internet of things (IIoT) is industrial machines connected to the enterprise cloud storage area for data storage as well as data retrieval. Industry 4.0 along with IIoT can make a big revolution in the management of global supply chain. Lean Six Sigma approach in global supply chain using Industry 4.0 and IIoT creates an ideal process flow that is highly optimized as well as perfect and free from defects and wastage. The models proposed in this paper make the supply chain fully autonomous.
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**** '2017 IJAT Best Review Paper Award' for the most prominent review paper in recent years published in the International Journal of Automation Technology (IJAT). ****
A fourth industrial revolution is occurring in global manufacturing. It is based on the introduction of Internet of things and servitization concepts into manufacturing companies, leading to vertically and horizontally integrated production systems. The resulting smart factories are able to fulfill dynamic customer demands with high variability in small lot sizes while integrating human ingenuity and automation. To support the manufacturing industry in this conversion process and enhance global competitiveness, policy makers in several countries have established research and technology transfer schemes. Most prominently, Germany has enacted its Industrie 4.0 program , which is increasingly affecting European policy, while the United States focuses on smart manufacturing. Other industrial nations have established their own programs on smart manufacturing, notably Japan and Korea. This shows that manufacturing intelligence has become a crucial topic for researchers and industries worldwide. The main object of these activities are the so-called cyber-physical systems (CPS): physical entities (e.g., machines, vehicles, and work pieces), which are equipped with technologies such as RFIDs, sensors, microprocessors, telematics or complete embedded systems. They are characterized by being able to collect data of themselves and their environment , process and evaluate these data, connect and communicate with other systems, and initiate actions. In addition, CPS enabled new services that can replace traditional business models based solely on product sales. The objective of this paper is to provide an overview of the Industrie 4.0 and smart manufacturing programs, analyze the application potential of CPS starting from product design through production and logistics up to maintenance and exploitation (e.g., recycling), and identify current and future research issues. Besides the technological perspective, the paper also takes into account the economic side considering the new business strategies and models available.
With rapid advancements in industry, technology and applications, many concepts have emerged in manufacturing. It is generally known that the far-sighted term ‘Industry 4.0’ was published to highlight a new industrial revolution. Many manufacturing organizations and companies are researching this topic. However, the achievement criteria of Industry 4.0 are as yet uncertain. In addition, the technology roadmap of accomplishing Industry 4.0 is still not clear in industry nor in academia to date. This paper focuses on the fundamental conception of Industry 4.0 and the state of current manufacturing systems. It also identifies the research gaps between current manufacturing systems and Industry 4.0 requirements. The major contribution is an implementation structure of Industry 4.0, consisting of a multi-layered framework is described, and is shown how it can assist people in understanding and achieving the requirements of Industry 4.0.
In recent years, there have been great advances in industrial Internet of Things (IIoT) and its related domains, such as industrial wireless networks (IWNs), big data, and cloud computing. These emerging technologies will bring great opportunities for promoting industrial upgrades and even allow the introduction of the fourth industrial revolution, namely, Industry 4.0. In the context of Industry 4.0, all kinds of intelligent equipment (e.g., industrial robots) supported by wired or wireless networks are widely adopted, and both real-time and delayed signals coexist. Therefore, based on the advancement of software-defined networks technology, we propose a new concept for industrial environments by introducing software-defined IIoT in order to make the network more flexible. In this paper, we analyze the IIoT architecture, including physical layer, IWNs, industrial cloud, and smart terminals, and describe the information interaction among different devices. Then, we propose a software-defined IIoT architecture to manage physical devices and provide an interface for information exchange. Subsequently, we discuss the prominent problems and possible solutions for software-defined IIoT. Finally, we select an intelligent manufacturing environment as an assessment test bed, and implement the basic experimental analysis. This paper will open a new research direction of IIoT and accelerate the implementation of Industry 4.0.
This article aims to provide a systematic and comprehensive comparison of the coverage of the three major bibliometric databases: Google Scholar, Scopus and the Web of Science. Based on a sample of 146 senior academics in five broad disciplinary areas, we therefore provide both a longitudinal and a cross-disciplinary comparison of the three databases. Our longitudinal comparison of eight data points between 2013 and 2015 shows a consistent and reasonably stable quarterly growth for both publications and citations across the three databases. This suggests that all three databases provide sufficient stability of coverage to be used for more detailed cross-disciplinary comparisons. Our cross-disciplinary comparison of the three databases includes four key research metrics (publications, citations, h-index, and hI, annual, an annualised individual h-index) and five major disciplines (Humanities, Social Sciences, Engineering, Sciences and Life Sciences). We show that both the data source and the specific metrics used change the conclusions that can be drawn from cross-disciplinary comparisons.
Big Data Analytics (BDA) and Internet of Things (IoT) are rising quickly. The recent emerging Industrial IoT (IIoT), a sub-paradigm of IoT, focuses more in safety-critical industrial applications. Studies showed that the adoption of BDA increase companies’ output and productivity; IoT enables companies to have more information and control in physical resources, processes, and environments; BDA and IIoT complement each other and develop as a double “helix”. In this position paper, we briefly review the opportunities and challenges in this era of big data and IoT for the Møre maritime cluster; then we propose a new framework integrating BDA and IIoT technologies for offshore support vessels (OSVs) based on a hybrid CPU/GPU/FPGA high performance computing platform. We believe that such a framework, when implemented, can help maritime companies increase their output and productivity, and hence enable the whole cluster to continue to be a leader in the global maritime industry.
Internet of Things (IoT) has provided a promising opportunity to build powerful industrial systems and applications by leveraging the growing ubiquity of radio-frequency identification (RFID), and wireless, mobile, and sensor devices. A wide range of industrial IoT applications have been developed and deployed in recent years. In an effort to understand the development of IoT in industries, this paper reviews the current research of IoT, key enabling technologies, major IoT applications in industries, and identifies research trends and challenges. A main contribution of this review paper is that it summarizes the current state-of-the-art IoT in industries systematically.
Agriculture is considered to be "climate-smart" when it contributes to increasing food security, adaptation and mitigation in a sustainable way. This new concept now dominates current discussions in agricultural development because of its capacity to unite the agendas of the agriculture, development and climate change communities under one brand. In this opinion piece authored by scientists from a variety of international agricultural and climate research communities, we argue that the concept needs to be evaluated critically because the relationship between the three dimensions is poorly understood, such that practically any improved agricultural practice can be considered climate-smart. This lack of clarity may have contributed to the broad appeal of the concept. From the understanding that we must hold ourselves accountable to demonstrably better meet human needs in the short and long term within foreseeable local and planetary limits, we develop a conceptualization of climate-smart agriculture as agriculture that can be shown to bring us closer to safe operating spaces for agricultural and food systems across spatial and temporal scales. Improvements in the management of agricultural systems that bring us significantly closer to safe operating spaces will require transformations in governance and use of our natural resources, underpinned by enabling political, social and economic conditions beyond incremental changes. Establishing scientifically credible indicators and metrics of long-term safe operating spaces in the context of a changing climate and growing social-ecological challenges is critical to creating the societal demand and political will required to motivate deep transformations. Answering questions on how the needed transformational change can be achieved will require actively setting and testing hypotheses to refine and characterize our concepts of safer spaces for social-ecological systems across scales. This effort will demand prioritizing key areas of innovation, such as (1) improved adaptive management and governance of social-ecological systems; (2) development of meaningful and relevant integrated indicators of social-ecological systems; (3) gathering of quality integrated data, information, knowledge and analytical tools for improved models and scenarios in time frames and at scales relevant for decision-making; and (4) establishment of legitimate and empowered science policy dialogues on local to international scales to facilitate decision making informed by metrics and indicators of safe operating spaces. Introduction: history of the concept of climate-smart agriculture
The Institute for Scientific Information's (ISI) citation databases have been used for decades as a starting point and often as the only tools for locating citations and/or conducting citation analyses. ISI databases (or Web of Science [WoS]), however, may no longer be sufficient because new databases and tools that allow citation searching are now available. Using citations to the work of 25 library and information science faculty members as a case study, this paper examines the effects of using Scopus and Google Scholar (GS) on the citation counts and rankings of scholars as measured by WoS. Overall, more than 10,000 citing and purportedly citing documents were examined. Results show that Scopus significantly alters the relative ranking of those scholars that appear in the middle of the rankings and that GS stands out in its coverage of conference proceedings as well as international, non-English language journals. The use of Scopus and GS, in addition to WoS, helps reveal a more accurate and comprehensive picture of the scholarly impact of authors. WoS data took about 100 hours of collecting and processing time, Scopus consumed 200 hours, and GS a grueling 3,000 hours.
https://www.igi-global.com/chapter/industry-40/211744 .............
Industry 4.0 can be considered the 21st century's industrial revolution and will soon be the new form of manufacturing delight. The definitive customer would experience manufacturing requests determined by artificial intelligence, machine learning, and automated technologies linked with data science support for gauging customer necessities. Phenomenally, Industry 4.0 is rapidly changing the firm's management and organizational systems, and competencies, as well as making its environment much more explored, even if more complexed than in the past. This new industrial revolution would possess systems with transformative technologies for managing interconnected systems between its physical assets and computational capabilities. Such enterprises would require skilled workforce to improve and operate advanced manufacturing tools and systems, and investigate the machine data, clients, and global capitals, resulting in an escalating need for trained employees proficient in cross-functional capacities and with competencies to cope new processes and IT systems.
Industrial Internet of Things (IIoTs) is the fast growing network of interconnected things that collects and exchange data using embedded sensors planted everywhere. Several IIoT applications such as the ones related to healthcare systems are expected to widely utilize the evolving 5G technology. This 5G-inspired IIoT paradigm in healthcare applications enables the users to interact with various types of sensors via secure wireless medical sensor networks (WMSNs). Users of 5G networks should interact with each other in a seamless secure manner. And thus, security richness is highly coveted for the real time wireless sensor network systems. Asking users to verify themselves before every interaction is a tedious, time-consuming process that disrupts inhabitants activities and degrades the overall healthcare system performance. To avoid such problems, we propose a Context-sensitive Seamless Identity Provisioning (CSIP) framework for the IIoT. CSIP proposes a secure mutual authentication approach using Hash and Global Assertion Value to prove that the proposed mechanism can achieve the major security goals of the WMSN in a short time period. IEEE
IIOT (Industrial Internet of Things) is a technology that combines industrial techniques, “smart sensors” and “smart devices” in a network. Such technology allows memorizing, analyzing and integrating data from all “smart sensors” to a single workspace. As part of IIoT it generally considered “intelligent enterprises” (transport, engineering, processing, etc.) and, in some ways, “intelligent machines” (cars, trucks, machines belonging to category of “public transportation” etc.). The distinction between “smart” and “ordinary” enterprises, as well as between “smart” and “ordinary” machines is considered by convention. However, it is clear that at some moment quantitative changes turn into qualitative and provide amazing results.
The Internet of Things (IoT) is an information network of physical objects (sensors, machines, cars, buildings, and other items) that allows interaction and cooperation of these objects to reach common goals [2]. While the IoT affects among others transportation, healthcare, or smart homes, the Industrial Internet of Things (IIoT) refers in particular to industrial environments. In this context Cyber Manufacturing Systems (CMS) evolved as a significant term. This opening chapter gives a brief introduction of the development of IIoT introducing also the Digital Factory and cyber-physical systems. Furthermore, the challenges and requirements of IIoT and CMS are discussed as well as potentials regarding the application in Industry 4.0 are identified. In this process aspects as economic impact, architectural pattern and infrastructures are taken into account. Besides, also major research initiatives are presented. In addition to that, an orientation to the reader is given in this chapter by providing brief summaries of the chapters published in this book. Hereby, the following research areas are addressed: “Modeling for CPS and CPS”, “Architectural Design Patterns for CMS and IIoT”, “Communication and Networking”, “Artificial Intelligence and Analytics”, and “Evolution of Workforce and Human-Machine-Interaction”. The chapter closes with a discussion about future trends of IIoT and CMS within Industry 4.0.
Industrial and IP-enabled low-power wireless networking technologies are converging, resulting in the Industrial Internet of Things. On the one hand, low-power wireless solutions are available today that answer the strict reliability and power consumption requirements of industrial applications. These solutions are based on Time- Synchronized Channel Hopping, a medium access control technique at the heart of industrial standards such as the WirelessHART and ISA100.11a, and layer 1 and 2 standards such as IEEE802.15.4e. On the other hand, a range of standards have been published to allow low-power wireless devices to communicate using the Internet Protocol (IP), thereby becoming true “fingers of the Internet,” and greatly simplifying their integration into existing networks. This article acknowledges the standardization effort to combine those capabilities. The networks resulting from this convergence exhibit reliability and power consumption performances compatible with demanding industrial applications, while being easy to integrate, and following the end-to-end paradigm of today's Internet. In particular, this article presents the work being done in 6TiSCH, a newly-formed working group in the Internet Engineering Task Force, which is standardizing the mechanisms making the Industrial Internet of Things a reality.
Since the late 1980s the world is working towards connectivity and convergence. In the last three decades, the convergence of information resources has happened. However to achieve a true convergence the information assets have to be shared, used and executed fruitfully by the various gadgets which we use in our daily lives. Internet of Things is a concept which leverages on the power of networks to create ubiquitous sensor-actuator networks. With the advent of the cloud technologies, the concept of IOTs can be integrated with even the basic elements having limited computing power. This paper aims to evaluate the possibilities offered by integrating the two concepts of IOTs and Cloud Computing.
Smart Manufacturing is the dramatically intensified and pervasive application of networked information-based technologies throughout the manufacturing and supply chain enterprise. The defining technical threads are time, synchronization, integrated performance metrics and cyber-physical–workforce requirements. Smart Manufacturing responds and leads to a dramatic and fundamental business transformation to demand-dynamic economics keyed on customers, partners and the public; enterprise performance and variability management; real-time integrated computational materials engineering and rapid qualification, demand-driven supply chain services; and broad-based workforce involvement. IT-enabled Smart factories and supply networks can better respond to national interests and strategic imperatives and can revitalize the industrial sector by facilitating global competitiveness and exports, providing sustainable jobs, radically improving performance, and facilitating manufacturing innovation.
The industrial internet of things (IIoT) applications and taxonomy
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IIoT Sensors: making the physical world digital
- P Potgieter
Convergence and commercial momentum-industrial internet of things evolution
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Actual status of data collection of production process and advantages of introduction of IoT
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People in the sightseeing area are smiling, people in charge of setting and people walking streets are also smiling
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February) Prospects for the development of IIOT technology in Russia
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Japan agency for medical research and development. Smart Cyber Operating Theater (SCOT)
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