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- Herausgeber: John Wiley & Sons
- Kategorie: Fachliteratur
- Sprache: Englisch
INTEGRATION OF MECHANICAL AND MANUFACTURING ENGINEERING WITH IOT The book provides researchers, professionals, and students with a resource on the basic principles of IoT and its applications, as well as a guide to practicing engineers who want to understand how the Internet of Things can be implemented for different fields of mechanical and manufacturing engineering. This book broadly explores the latest developments of IoT and its integration into mechanical and manufacturing engineering. It details the fundamental concepts and recent developments in IoT & Industry 4.0 with special emphasis on the mechanical engineering platform for such issues as product development and manufacturing, environmental monitoring, automotive applications, energy management, and renewable energy sectors. Topics and related concepts are portrayed comprehensively so that readers can develop expertise and knowledge in the field of IoT. It is packed with reference tables and schematic diagrams for the most commonly used processes and techniques, thereby providing a resource on the basic principles and application of IoT in manufacturing sectors. Audience The book will be read by academic researchers, industry engineers, and R&D personnel in materials, information and technology, artificial intelligence, and manufacturing. The book will greatly assist graduate students.
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Veröffentlichungsjahr: 2023
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Table of Contents
Cover
Series Page
Title Page
Copyright Page
Dedication Page
Preface
1 Evolution of Internet of Things (IoT): Past, Present and Future for Manufacturing Systems
1.1 Introduction
1.2 IoT Revolution
1.3 IoT
1.4 Fundamental Technologies
1.5 IoT Architecture
1.6 Cloud Computing (CC) and IoT
1.7 Edge Computing (EC) and IoT
1.8 Applications of IoT
1.9 Industry 4.0 Integrated With IoT Architecture for Incorporation of Designing and Enhanced Production Systems
1.10 Current Issues and Challenges in IoT
1.11 Conclusion
References
2 Fourth Industrial Revolution: Industry 4.0
2.1 Introduction
2.2 Evolution of Industry
2.3 Basic IoT Concepts and the Term Glossary
2.4 Industrial Revolution
2.5 Industry
2.6 Industry Production System 4.0 (Smart Factory)
2.7 I4.0 in Functional Field
2.8 Existing Technology in I4.0
2.9 Applications in Current Industries
2.10 Future Scope of Research
2.11 Discussion and Implications
2.12 Conclusion
References
3 Interaction of Internet of Things and Sensors for Machining
3.1 Introduction
3.2 Various Sensors Involved in Machining Process
3.3 Other Sensors
3.4 Interaction of Sensors During Machining Operation
3.5 Sensor Fusion Technique
3.6 Interaction of Internet of Things
3.7 IoT Technologies in Manufacturing Process
3.8 Industrial Application
3.9 Decision Making Methods
3.10 Conclusion
References
4 Application of Internet of Things (IoT) in the Automotive Industry
4.1 Introduction
4.2 Need For IoT in Automobile Field
4.3 Fault Diagnosis in Automobile
4.4 Automobile Security and Surveillance System in IoT-Based
4.5 A Vehicle Communications
4.6 The Smart Vehicle
4.7 Connected Vehicles
4.8 Conclusion
References
5 IoT for Food and Beverage Manufacturing
5.1 Introduction
5.2 The Influence of IoT in the Food Industry
5.3 A Brief Review of IoT’s Involvement in the Food Industry
5.4 Challenges to the Food Industry and Role of IoT
5.5 Applications of IoT in a Food Industry
5.6 A FW Tracking System Methodology Based on IoT
5.7 Designing an IoT-Based Digital FW Monitoring and Tracking System
5.8 The Internet of Things (IoT) Architecture for a Digitized Food Waste System
5.9 Hardware Design: Intelligent Scale
5.10 Software Design
References
6 Opportunities: Machine Learning for Industrial IoT Applications
6.1 Introduction
6.2 I-IoT Applications
6.3 Machine Learning Algorithms for Industrial IoT
6.4 I-IoT Data Analytics
6.5 Conclusion
References
7 Role of IoT in Industry Predictive Maintenance
7.1 Introduction
7.2 Predictive Maintenance
7.3 IPdM Systems Framework and Few Key Methodologies
7.4 Economics of PdM
7.5 PdM for Production and Product
7.6 Implementation of
IPdM
7.7 Case Studies
7.8 Automotive Industry—Integrated IoT
7.9 Conclusion
References
8 Role of IoT in Product Development
8.1 Introduction
8.2 Need to Understand the Product Architecture
8.3 Product Development Process
8.4 Conclusion
References
9 Benefits of IoT in Automated Systems
9.1 Introduction
9.2 Benefits of Automation
9.3 Smart City Automation
9.4 Smart Home Automation
9.5 Automation in Manufacturing
9.6 Healthcare Automation
9.7 Industrial Automation
9.8 Automation in Air Pollution Monitoring
9.9 Irrigation Automation
References
10 Integration of IoT in Energy Management
10.1 Introduction
10.2 Energy Management Integration with IoT in Industry 4.0
10.3 IoT in Energy Sector
10.4 Provocations in the IoT Applications
10.5 Energy Generation
10.6 Conclusion
References
11 Role of IoT in the Renewable Energy Sector
11.1 Introduction
11.2 Internet of Things (IoT)
11.3 IoT in the Renewable Energy Sector
11.4 Data Analytics
11.5 Conclusion
References
Index
End User License Agreement
List of Tables
Chapter 3
Table 3.1 Respective signal sensor in various machining monitoring process and...
Table 3.2 Sensors used in milling operation.
Table 3.3 Different sensors used in turning machining operation.
Table 3.4 Sensors in drilling machining.
Table 3.5 Various data-driven methods and algorithms used [4].
Table 3.6 Merits and demerits of various AI approaches.
Chapter 4
Table 4.1 Various Sensor technology, wireless protocols, and their focus in Io...
Chapter 6
Table 6.1 Commercial I-IoTs.
Table 6.2 Comparison of ML algorithms.
List of Illustrations
Chapter 1
Figure 1.1 Power generating capacity installed in 2017 [5, 6]. https://www.avs...
Figure 1.2 Power generating capacity installed in 2017 [5, 6].
Figure 1.3 IoT Architecture from [32].
Figure 1.4 Architecture of CC adopted from [32].
Figure 1.5 Architecture of CC adopted from [9].
Figure 1.6 EC with IoT architecture [71].
Figure 1.7 An IoT diagram depicting application from various sectors based on ...
Figure 1.8 IoT-based manufacturing process from [6].
Figure 1.9 Interoperability of design and production systems in a multistage I...
Figure 1.10 IoT architecture [132].
Figure 1.11 Node failures due to lack of IoT functions from [32].
Chapter 2
Figure 2.1 Integrated I4.0.
Figure 2.2 The Industrial Revolution [18] Source: (Tay
et al
., 2018).
Figure 2.3 Conceptual framework [21] Source: (Zheng
et al.
, 2021).
Figure 2.4 Product planning software—schema with connections.
Figure 2.5 PP software—control and optimization criteria [25], Source: (Wollsc...
Figure 2.6 Industry 4.0 smart factory [20], Source: (Rojko, 2017).
Figure 2.7 I4.0 support and operational level control in the industry [28], Or...
Figure 2.8 Production Order Graphical Presentation [20], Source: (Rojko, 2017)...
Figure 2.9 I4.0 technologies theoretical framework.
Figure 2.10 Framework for summarizing the findings of I4.0 adoption patterns.
Chapter 3
Figure 3.1 MiniDyn Dynamometer and 3 component force link [39].
Figure 3.2 Triaxial accelerometer.
Figure 3.3 Acoustic emission signal during machining process.
Figure 3.4 Types of sensors involved in milling operation [52].
Figure 3.5 Thermocouple sensor used in turning operation [53].
Figure 3.6 Drilling operation with rotating cutting dynamometer [54].
Figure 3.7 Sensor fusion model approach [55].
Figure 3.8 Machining operation state [21].
Figure 3.9 Machine shop application.
Figure 3.10 Integrated structure of machining and monitoring system [57].
Figure 3.11 Incorporation of hardware and software system [57].
Figure 3.12 Structure of neurofuzzy models [4].
Chapter 4
Figure 4.1 Application of IoT in automobile.
Figure 4.2 Global IoT automotive market.
Figure 4.3 Different sensors used in car.
Figure 4.4 Security and survelliance in IoT system [28].
Figure 4.5 Vehicle connectivity in V2X technology [37].
Figure 4.6 Vehicle to vehicle communication using IoT wireless networks [39].
Figure 4.7 Illustration of vehicle to infrastructure (V2I) communication.
Figure 4.8 Architecture of vehicle to infrastructure.
Figure 4.9 Vehicle to pedestrian communication in intelligence transportation ...
Figure 4.10 System architecture of vehicle to device communication.
Chapter 5
Figure 5.1 Water monitoring IoT architecture.
Figure 5.2 Design of IoT based FW tracking system.
Figure 5.3 Reference architecture.
Figure 5.4 IoT-based FW monitoring intelligent scale.
Figure 5.5 Visual user interface of FW tracker.
Figure 5.6 Collected data in excel format.
Figure 5.7 Dashboard for real-time FW monitoring.
Chapter 6
Figure 6.1 Components for making a smart device.
Figure 6.2 Important parts of I-IoT system.
Figure 6.3 Applications of I-IoT.
Figure 6.4 Industrial IoT solution. Courtesy: 7Devs.co.
Figure 6.5 Supervised learning process in machine learning.
Figure 6.6 Support vector machine [11].
Figure 6.7 Semisupervised learning process in machine learning.
Figure 6.8 Unsupervised learning process in machine learning.
Figure 6.9 Reinforced learning process in machine learning.
Chapter 7
Figure 7.1 Simplified scheme of an IoT platform with Arduino board [9].
Figure 7.2 The architecture for integrated predictive maintenance (IPdM) techn...
Figure 7.3 IoT-integrated automotive industry [47].
Figure 7.4 Layout of Ash evacuator (station design and layout—British Electric...
Chapter 8
Figure 8.1 Industrial revolutions happened in earlier decades [10].
Figure 8.2 Primary drivers of Industry 4.0.
Figure 8.3 Product design steps.
Figure 8.4 General elements of IoT devices [19].
Figure 8.5 New product development process steps.
Figure 8.6 Product configuration functions.
Figure 8.7 Evaluation of manufacturing sector.
Chapter 9
Figure 9.1 Components of smart city.
Figure 9.2 Working structure of home automation.
Figure 9.3 Flowchart of block chain implementation.
Figure 9.4 IoT applications in healthcare.
Figure 9.5 Fog-based architecture for healthcare applications.
Figure 9.6 The essentials of Industrial IoT.
Figure 9.7 Air pollution monitoring.
Chapter 10
Figure 10.1 Energy supply chain [19].
Figure 10.2 Energy harvesting system [24].
Figure 10.3 Share of residential energy consumption [19].
Figure 10.4 Integrated smart energy system [24].
Figure 10.5 IoT employment in various sectors [43].
Figure 10.6 Challenges in IoT based smart city concept [46].
Figure 10.7 Workflow for thermal energy generation [66].
Figure 10.8 Self-powered vibration sensor [85].
Figure 10.9 Piezoelectric energy harvester [97].
Figure 10.10 Wind energy to electric energy generation with the aid of piezoel...
Figure 10.11 Workflow of RF energy harvester [24].
Chapter 11
Figure 11.1 Components of IoT platform.
Figure 11.2 A solar monitoring architecture based on the IoT paradigm.
Figure 11.3 Monitoring system in the plant (a) before IoT integration, (b) aft...
Figure 11.4 IoT-based solar energy harvesting solution.
Figure 11.5 A conceptual model of smart grid.
Figure 11.6 Five interoperability layers, domains, and zones of the SGAM model...
Figure 11.7 Evolution of smart grid 2.0.
Figure 11.8 IoT and smart homes.
Figure 11.9 Smart grids connected with smart devices from the demand and suppl...
Guide
Cover Page
Series Page
Title Page
Copyright Page
Dedication Page
Preface
Table of Contents
Begin Reading
Index
WILEY END USER LICENSE AGREEMENT
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Scrivener Publishing100 Cummings Center, Suite 541JBeverly, MA 01915-6106
Publishers at ScrivenerMartin Scrivener ([email protected])Phillip Carmical ([email protected])
Integration of Mechanical and Manufacturing Engineering with IoT
A Digital Transformation
Edited by
R. RajasekarC. MoganapriyaP. Sathish Kumar
and
M. Harikrishna Kumar
This edition first published 2023 by John Wiley & Sons, Inc., 111 River Street, Hoboken, NJ 07030, USA and Scrivener Publishing LLC, 100 Cummings Center, Suite 541J, Beverly, MA 01915, USA© 2023 Scrivener Publishing LLCFor more information about Scrivener publications please visit www.scrivenerpublishing.com.
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Library of Congress Cataloging-in-Publication Data
ISBN 978-1-119-86500-1
Cover image: Pixabay.ComCover design by Russell Richardson
This book is dedicated to the profound memory of our beloved friend Mr. R. Manivannan
31st October 1989 - 4th July 2022
Preface
The Internet of Things (IoT) changes the way in which products are designed, prototyped and manufactured. In recent times, the core mechanical companies have experienced a strong transition on controlling mechanical systems by software-driven tools. Technology driven platform transform products into IoT-led smart devices, which can communicate with the producer, when they departed from the manufacturing line. Manufacturers should upgrade their production strategy by linking with IoT in order to maintain a long term sustainability.
This book is intended to compile and broadly explore the latest developments of IoT and its integration towards mechanical and manufacturing engineering. The book is envisioned to explore the fundamental concepts and recent developments in IoT & Industry 4.0 with special emphasis to the mechanical engineering platform to such issues as product development and manufacturing, environmental monitoring, automotive applications, energy management and renewable energy sectors. Topics and related concepts are portrayed in a comprehensive manner so that readers can develop expertise and knowledge in the field of IoT. It will provide professionals and students with a resource on the basic principles and application of IoT in manufacturing sectors.
We thank all the authors for their valuable research inputs. We render our sincere thanks to Scrivener – Wiley publishing team for their help with this book.
R. Rajasekar
C. Moganapriya
P. Sathish Kumar
M. Harikrishna Kumar
1Evolution of Internet of Things (IoT): Past, Present and Future for Manufacturing Systems
Vaishnavi Vadivelu1, Moganapriya Chinnasamy2, Manivannan Rajendran3, Hari Chandrasekaran1 and Rajasekar Rathanasamy3*
1Department of Management Studies, Kongu Engineering College, Erode, Tamil Nadu, India
2Department of Mining Engineering, Indian Institute of Technology Kharagpur, West Bengal, India
3Department of Mechanical Engineering, Kongu Engineering College, Erode, Tamil Nadu, India
Abstract
The Internet of Things (IoT) is a platform that permits communication between gadgets, elements, and other digitized resources that send and receive data automatically without involvement of personal interactions. The key feature of IoT is the massive amount of information generated by finished systems that must be interpreted in the cloud in a short amount of time. The current study reveals the origin of IoT, brief revolution of IoT over two decades, emerging technologies with IoT, its current applications, and its future challenges. The current part of chapter starts with a broad review of the IoT revolution. After that, the technical aspects of IoT enablement technologies, protocols, and applications. The current chapter goal is to provide a more comprehensive overview of the most important protocols and application issues, allowing researchers and application developers to quickly grasp how various protocols interact to deliver desired functionalities without having to wade through RFCs and standard specifications.
Keywords: IoT, EC, manufacturing system, cloud computing, IoT architecture
1.1 Introduction
The current world is being pushed by the Internet and digital era, which is affecting digital life notions. The online and wi-fi networks have performed a crucial part in the digital era. In today’s fact changing world, the major goal is to use modern technologies to minimize human-machine contact efforts while increasing machine-machine interaction capabilities [1]. Information and communication techniques has formed a trend in this regard, bringing concepts such as wireless control, remote monitoring, and so on, reducing the strain on humans and workers. An innovative and revolutionary technology known as the IoT was launched as a result of developments in wireless communications, cognitive computing and network connection [2]. Wireless sensor networks (WSN), bluetooth, long-term evolution (LTE), radiofrequency identification (RFID), near-field communication (NFC), as well as various wireless modern communications link things to the Internet. As a result, IoT might be described as “things connected via the Internet” [3]. The IoT links trillions of electronic things and electronic objects to develop a digital environment that allows humans to use new cyber technology to sense, analyse, regulate, and improve old physical manual systems [4]. A shipyard’s operating efficiency was improved using Industrial Internet of Things (I-IoT) principles [5, 6].
Over the last 20 years, it has attracted a wide spectrum of audiences from business and academics, expanding the technology to a variety of scientific applications in various industries. IoT principles are applied in various fields like agricultural, supply of water, smart grid and energy savings, handling equipment and materials, industrial businesses, and transport planning. The Internet of Things concept has gained widespread acceptance and use in a variety of sectors. However, the IoT-related study findings do not delve into the IoT’s fundamental development principles and research trends. Few studies had been done to disclose the beginnings of the IoT, analyze its popular study themes with the emphasize the challenges that the IoT will confront in the future. Furthermore, the advancement of IoT technology is inextricably linked to the support of associated theories and methodologies, and a rising number of researchers and practitioners are keen to learn more about the IoT’s current state of development through reading publications.
1.2 IoT Revolution
The word Internet of Things was changed for M2M (Machine to Machine), which was anticipated and referenced by MIT professors who characterized the future world of communication in the late 1990s. Briton Kevin Ashton, a creative developer, was making a presentation for Procter & Gamble in which he presented IoT as a system that connects multiple devices using RFID tags for managing their supply chain. The adoption of RFID has enabled for the direct flow of information between devices to be accelerated. He envisioned a vision of data being collected, analyzed, and transferred with the absence of human interference. The IoT is a network system enabled by the Internet that aims to create real-time interaction between objects, machinery, and people using a number of advanced techniques. Likewise, a number of significant advancements aided in the IoT’s development are depicted in Figure 1.1.
The first was an Internet-connected refrigerator developed by LG Electronics in 2000, which allowed customers to purchase online and conduct video chats. Another significant advancement was the creation in 2005 of Nabaztag, a little rabbit-shaped robot capable of providing up-to-date news, weather forecasts, and stock market updates. The Auto-ID center sponsored 103 branches throughout the world and created a standard to maintain the smart package to communicate with the other networks at distributors and buyers. Over time, the market improved, investments improved, chips improved, and chips grew cheaper and cheaper.Nest Labs was the first company to develop a sensor technology based, wireless based,self education, thermostat and smoke alarm to introduce IoT in 2010. The IoT was ultimately brought to the public’s attention when, Google bought Nest Labs in 2014 and debuted the Amazon Alexa and, later, Google Home. Since then, the sector has been growing faster.
Figure 1.1 Power generating capacity installed in 2017 [5, 6]. https://www.avsystem.com/blog/what-is-internet-of-things-explanation/.
Figure 1.2 Power generating capacity installed in 2017 [5, 6].
IoT has gotten a lot of attention during the last 20 years, with a lot of government officials, business leaders, and academics believing that this essential technology is evolving people’s standard of living and surrounding conditions are depicted in Figure 1.2 [7–10]. Several researchers have stretched IoT associated research with service as Internet of Service [11], equipment as Internet of Machine [12], humans as Internet of People [13] and information as Internet of Knowledge [14, 15]. With the advancement of science and technology, IoT is expected to have a wide range of applications in the government service and domestic sectors [16]. The application of IoT helps to reduce the pollution created by human activities and enhance the economic growth of the country [17–19]. To apprehend this possible development in the economic, the development of diverse developing technologies and services must keep up with the expansion of market demand [14, 20, 21]. The IoT diversified developments in the aspect of technology, applications, undamental ideologies, design aspects, and trends in the anticipated growth are combined with interdisciplinary association with the telecommunications, electronics and informatics [7, 9, 22]. After the two decades of development in IoT, the research have been extended, accepted in numerous industries and employed in smart medical care, smart agriculture, smart supply chains and smart cities [23–28].
1.3 IoT
The advancement of mobile gadgets, automobiles and integrated device has aided in the creation of a smart world of linking the gadgets that can perceive, gather data, cooperate, and make choices without the need for human intervention [29]. This intelligence is called Internet for things, which simplifies the day-to-day life of humans. The IoT is characterized as a dynamic, self-configuring manual linkage and virtual devices connected through interoperable communication, media and standards [26, 30, 31]. Wi-Fi, Bluetooth, Zig Bee and other protocols are used by these items to communicate with one another. The interconnection of numerous communication technological innovations which includes wireless network and sensors, controller networks, tracking and identification networks and so on to promote interactivity and cooperation between them is a crucial component of the IoT [32]. Some emerged real-time examples are wearable fitness and trackers (like Fitbits) and IoT healthcare apps, voice assistants (Siri and Alexa), smart automobiles (Tesla) and smart appliances.
1.4 Fundamental Technologies
Hardware such as sensing devices, electronic controls and integrated sensor hardware; software components such as data storage requirement and information processing analysis of data predictive analysis and visualization; and demonstration as unique, simple to use visualization and explanation tools technique that can be accessible across multiple platforms and built for a variety of implementations fields are the three IoT components that allow for smooth widespread computing [9]. The current part discusses a few enabling technologies that contribute to the components of IoT.
1.4.1 RFID and NFC
The key innovation in RFID technology is the design and development of a wireless microchip for the embedded communication paradigm for data transfer [33]. It provides the automated identification of whatever is connected to this electronic barcode [34, 35]. RFID devices, often known as RFID tags, which used microchips to transmit data wirelessly. RFID tags emit data over the air, and an RFID reader recovers the signal, allowing items to be identified based on the data received (barcode). The most often utilized device for IoT applications in many industries such as retail, supply chain, healthcare, banking, privacy control, and social applications [36]. NFC is a quick sequence of high frequency network technology that exchanges data between a few centimetres, making it easier for people to use their phones and providing a variety of loyalty applications such as locking and unlocking doors and cars, exchanging contact information, paying for general populace transportation, reading newspapers, and others [37].
1.4.2 WSN
WSN is a broad system of intelligent detecting device that gather, process, analyse, and transmit information [38]. The WSN is made up of the components listed below. To begin, the gather unit is referred to as a sensor, and it is responsible for collecting data in the form of waves and converting it into electronic knowledge that the formulating unit can understand. The second component is the processing unit, which is in charge of analyzing the recorded data. The transmission unit, which is in charge of all data transmission and receiving, is the third component. Finally, there is a power main controller that is a critical network component [39]. WSNs are also employed in a range of applications, including monitoring systems (e.g., surveilling pollution, disasters, and wildfires), industrial (e.g., intelligent lighting control, automation), defence, and healthcare applications [40].
1.4.3 Data Storage and Analytics (DSA)
A big storage unit is necessary since a great volume of data is produced and exchanged in IoT. As a result, data storage has become an important issue in the Internet of Things. To ensure effective communication, digital cities, intelligent and interconnected societies, and better medical are just a few of the data processing and storing technologies available. Cloud-based information management and processing have grown in popularity in recent years, and they are widely used and desired because they can speed up information analysis and deliver a highly secure interchange of information [38].
1.5 IoT Architecture
To manage the trillions and millions of linked devices, the IoT requires a flexible layered architecture. The diverse models originate from an increasing diversity of latent designs [41], and IoT-A is attempting to establish a common architecture to meet industrial demands, which is being investigated by the researcher [22]. Three-layer designs are the most typical IoT architectures because they are versatile, practical, and simple execution. The perception layer, network layer, and application layer are the three tiers of the architecture. Figure 1.3 depicts a three-layer IoT design, with an explanation underneath.
The perception layer has sensing capabilities, which means it gathers as well as accumulates specific data about the surrounding environment in which digital things are present.
Figure 1.3 IoT Architecture from [32].
The network layer is accountable for qualifying transmission of data and processing across several gadgets connected through the Internet.
The major function of the application layer is to deliver the ultimate user with a particular service relying on the specific application.
1.6 Cloud Computing (CC) and IoT
The growing emergence of interlinked devices throughout the world and changes in users and applications demand for massive amounts of data processing, prompted the development of a set of unique technologies that enable quick data processing and dependable services. One of these technologies is CC [32]. CC is a highly adaptable and extensible technology that enables a variety of services for IoT applications. Cloud is an innovative platform which aims to deliver a various service to the final customers [42]. The services include such as data storage choices, software strategies and analysis, a suitable platform, and fundamental development infrastructural facilities. Private, public, hybrid, and community clouds are all examples of cloud implementations. A large number of people use the public cloud via the Internet. The private businesses utilize the private cloud and designed a community cloud for specific set of organizations [43, 44]. And hybrid cloud is designed to control the cost and issues related to control [45]. The cloud capacity to manage enormous volumes of information utilizing data analytics, visualize the huge data by users and machine learning [46]. Specific required individual service, comprehensive network access, resource sharing, fast adaptability, and quantifiable services are some of the unique qualities of CC [47].
Self-service on requirement without needing human interaction, a customer can provide computer competences includes as network storage and processing time are desirable.
Broad network access refers to the availability of a wide range of network capabilities that may be accessible through standard processes to encourage the utilization of diversified thick client platforms such as mobile, laptops, tablets and workstations.
Storage, compute, memory, and network bandwidth are pooled to serve several users in a multitenant paradigm in which physical and virtual resources of various types are dynamically assigned and reassigned based on demand.
Rapid elasticity refers to the capacity to dynamically allocate and release resources in response to demand at any point in time.
To automatically control and optimize resource utilization, cloud systems use metering capabilities at some level of abstraction, such as storage, processing, bandwidth, and active user accounts.
1.6.1 Service of CC
The cloud system utilized the specific type of service which is the primary categories of cloud architecture. The cloud architecture is described in Figures 1.4 and 1.5 with explained below;
Platform as a Service (PaaS): A toolkit makes the PaaS available to application developers as a development environment. PaaS allows one (user) to deploy self-created or bought apps into cloud infrastructure utilizing the provider’s tools and programming language.
Software as a Service (SaaS): The SaaS paradigm simplifies the utilization of cloud-based application to access the variety of utilizer gadgets via a tinny buyer line such as a web browser or a programme interface.
Infrastructure as a Service (IaaS): According to the Internet Engineering Task Force, an (IaaS) distributer’s delivers online or physical systems/equipment and other resources. One can have access to processing electricity, storage facility and further computer assistance with IaaS.
Figure 1.4 Architecture of CC adopted from [32].
Figure 1.5 Architecture of CC adopted from [9].
1.6.2 Integration of IoT With CC
IoT and CC are dual major knowledge that donate to human routine lives by allowing IoT users to access a variety of services. Researchers are focusing on integrating the cloud and IoT, which have both realized quick growth and may be regarded complimentary to one another [48–51]. The incorporation of IoT and cloud would have high storage, processing and networking capabilities under a feature of IoT. The types of devices, technologies, and protocols of IoT utilize the scalability, interoperability, reliability, efficiency, and other IoT qualities are difficult to achieve. The integrating the IoT with cloud would overcome the above difficult concerns and have other benefits like ease-of-access, ease-of-use, and so on [52, 53]. The cloud could be a best part in handling the IoT services and creating the applications and services [48, 54].
The Cloud IoT paradigm, which combines cloud and IoT interoperability, aids in the development of new cloud-based intelligent applications and services, such as Sensing as a Service (SaaS), Sensing and Actuation as a Service (SAaaS), Sensor Event as a Service (SEaaS), Sensor as a Service (SenaaS), DataBase as a Service (DBaaS), Data as a Service (DaaS), Ethernet [55–57]. Healthcare, smarter house and digital monitoring, video surveillance, automobile and digital mobility, smart logistics, and environmental sensing are just a few of the applications that have evolved from these services [54, 56, 58–61].
1.7 Edge Computing (EC) and IoT
New applications, such as streaming video and virtual games, necessitate a quick response time [62, 63]. Similarly, energy usage is a major concern in wireless communication due to the limited resources of IoT devices, which are unable to perform calculations and computing locally. Moreover, the information must be transmitted to the cloud data centre, which takes time and may affect end-user QoS and experience. EC aspires to become the next IoT solution, capable of overcoming a variety of challenges such as duration-constrained and compiler optimization applications. EC denotes that enabling technologies that enable computing at the network’s edge, on both downstream and upstream data for cloud and IoT applications [62]. Any computational and network resources positioned between data sources and cloud data centres are referred to as “edge.” The edge between body things and the cloud, for example, is a smart phone; the edge between house things and the cloud is a gateway in a smart home; and the edge between a mobile device and the cloud is a mini data centre and a cloud let [64]. EC in the field of Industrial IoT adds agility, real-time processing, and autonomy to produce value for intelligent production [65]. Minimizing networking load and transmission delay, separating the monopoly of big inventors, delivering small and medium inventors with each and every possible chance to help cultivate advance innovations, minimizing energy consumption of mobile nodes and abolishing congestion within the core network as well as allowing greater durability, security and privacy protection are some of the benefits of processing data at the network edge [66, 67].
1.7.1 EC with IoT Architecture
EC refers to the activities of IoT devices at the network’s edge or limit that are linked to the distant Cloud [68]. Due to the huge increased amount of IoT devices, numerous organizations offer several IoT designs from various angles, and EC was already highlighted as an essential guidance for IoT systems [9, 69, 70]. The latest research on the subject attempted to demonstrate that EC architectures are the best solution to decrease latency, improving secrecy, and cutting bandwidth costs in IoT-based applications. The EC with IoT architecture is developed by [71] and shown in Figure 1.6.
The edge-computing IoT architecture is made up of four primary components such as the IoT final device, cloud, the edge and users. The architecture is designed with both available resources and the individual characteristics of each party in mind. Users use sophisticated IoT apps to improve their lives simpler, and instead of interacting directly with IoT end hardware, they frequently contact with them via interactive interfaces provided by the cloud or the edge. End devices for IoT are firmly implanted in the real environment. The IoT end device monitor the lively environment and perform actions to regulate it, but they lack sophistication in computation-intensive activities. Although the cloud has essentially infinite resources, it is frequently located far distant from end devices. As a result, a cloud-centric IoT system is unlikely to perform well [72], especially where actual data is required. Because the edge is such an important element of the entire architecture, it may integrate the two and coordinate the other three parties to collaborate and complement the cloud and IoT end devices for the best possible performance.
Figure 1.6 EC with IoT architecture [71].
1.8 Applications of IoT
In terms of adaptability, the IoT offers enormous opportunities for societal, environmental and economic implications. IoT have been adopted in various potential fields such as social, environmental, and economic. The fields like mobility, smart grid, industrial processing, agriculture and breeding, smart residential, medical and healthcare, societal security and environment production, and independent living. The above-mentioned applications are utilized by everyone in their day-to-day life. The usage of the above-mentioned applications is very essential and gain the much importance in the recent years. Figure 1.7 demonstrates the applications of IoT in various different fields.
1.8.1 Smart Mobility
Smart Mobility is a method of enabling seamless, effective, and flexible mobility throughout several modes. Due to the passage of time and the rising need of society, Vehicular Ad-hoc Networks (VANETs) have grown and gained a great deal of recognition. As a result, one could argue that it marks a fundamental shift toward a highly adaptable and cross transportation network. The Internet of Vehicles (IoV) is a new technology that promises to enhance road safety by safeguarding or minimizing incidents and allowing new optimum mobility modes. Many scholars have tackled additional challenges, such as traffic and travelling from one location to another, in an effort to improve [3].
Figure 1.7 An IoT diagram depicting application from various sectors based on data from [9].
The various developments from the researchers are IoT below smart mobility field with technical challenges associated to wireless communications [73]; development of Advanced Driver Assistant Systems (ADAS) to provide a direction and arranging and rectifying the multimedia and connectivity issues systems [74]; smart city and smart mobility is created to handle the Eco-Conscious Cruise Control for Public Transportation [75]; development and demonstration of virtual real objects, such as sensors are interrelated with the Virtual Object (VO) framework to monitor the transport [76]; electronic vehicle is connected with the electric vehicle collaboration into the Internet of Energy (IoE) for the Smart Grid infrastructure [77]; cloud with IoT [78]; cloud integration [79]; handled the whole network was affected by road traffic delays at one place [80, 81]; use of mobile sensors to identify a variety of metropolitan transportation modes might generate data that could then be categorized to better understanding the amount of behavioral alterations in travel [82]; applications for safety is developed to deliver messages and warnings regarding accidents, curve speeds, traffic infractions, and pre-crash detection [83]; convenience applications is created to help in personal routing and provide guidance in handling critical situation, out of network and power failure [84]; smart parking system uses a wireless connection or a publish subscribe communication paradigm to notify smart automobiles about availability parking spots in the location [32].
1.8.2 Smart Grid
A smart grid is an electricity distribution system that monitors and responds to regional demand fluctuations using online communications technologies [85]. It’s also recognized as a digitized technology which allows both-way communication, allowing ultimate consumers to request power after conducting examinations using sensors and receiving power in return. The grid distributes energies in accordance with the estimated demand, which has been established [86–88]. This information is analyzed by Supervisory Control and Data Acquisition (SCADA), a centralized server that issues urgent instructions, answers to modification requests to improve the stability, and protects the electricity system [89, 90].
The other allied developments to support the smart grid includes as measuring the energy efficiency, smart infra-structure and a forceful ecosystem [91]; implementation of effective monitoring system [92, 93]; creation of Automatic Meter Reading (AMR) and Advanced Metering Infrastructure (AMI) to record the utilization electricity without manpower [94]; integrating power generators (such as solar panels, wind farms, and other alternate energy sources) with the main power system to provide centralized control of the power network [95, 96].
1.8.3 Smart Home System
A Smart Home is a residential area that, like any other home, is outfitted with heating, lighting, and other technological devices. They can be controlled remotely using a smartphone or computer, which is a significant difference [3]. The smart home aims to provide adaptive monitoring of different electronic gadgets linked to houses, such as televisions, refrigerators, cookers, air conditioning units and so on [97]. Home automation services are used to control air conditioners, air cleaners, and curtain activities; residential protection facilities are used to prevent gas leaks and identify potential criminal activities; and residential managerial services are used to intelligently control smart devices such as cookers and televisions [98]. IoT is integrated with RFID [99, 100], cloud [101, 102] and EC [103, 104] to create a smart home system effectively.
1.8.4 Public Safety and Environment Monitoring
Public safety and environmental monitoring are the technique of conducting evaluations on seasonal changes, threatened species preservation, monitoring the quality of water and a variety of other qualities that are directly or indirectly connected to our environment. Various applications are associated with numerous sensors and other monitoring equipment to observe timely changes in environmental parameters [3]. The utilization of IoT to monitor the sensing qualities that open up an unimaginable market of possibilities [105]; security procedure is generated for monitoring the environmental by utilizing mobile WN [106]; utilizing RFID for monitoring the environmental [107]; Scanning of the environment is done through IP, with WSN for associating with many sensors, resulting in a rapid adoption, longer lifetime, excellent quality, and cheap maintenance [108]; established an Environment Internet of Things (EIoT) to track important factors such as soil, air, water and wind [109]; environmental monitoring and management, a combination of CC with IoT, Geoinformatics (Geographical Information System (GIS), Global Positioning System (GPS), Remote Sensing (RS) and e-science are utilized [110].
1.8.5 Smart Healthcare Systems
Because of IoT-enabled devices, continuous remote monitoring in the medical business is now possible, allowing physicians to give better treatment while keeping patients safe and healthy. Patients’ engagement and happiness have increased as interaction with doctors has become simpler and more efficient. IoT has altered the sanitary system and evolved it into a predicting and smart network by analyzing huge data and integrating various IoT devices to capture real-time physiological data of patients such as glucose levels in the blood, temperature monitors, and other critical data [111]. The IoT has the ability to relieve pressure on hygienic services while also offering tailored healthcare facilities to enhance people’s standard of living [112].
Data management is a delicate topic in healthcare systems, since client information contains critical and confidential information that must be assessed and handled in a timely and safe manner [113]. Remote Patient Monitoring (RPM) enables patients to be monitored irrespective of where they are and caregivers and family members may be contacted from a distance [114, 115]. Data acquisition, display and diagnostics are the three elements that make up the RPM system. Sensors are attached to the patients (for example, a blood sugar level) to gather data, which is then sent from the patient’s android mobile device to the handling data unit (diagnostics component) for analysis. The IoT is a hot topic in current trends and also provide solutions to handle the difficulty of exposure to healthcare services, the growing ageing demographic with medical problems and their necessity for remote patient monitoring, rising hospital expenses, and a desire for telehealth in developing nations [112].
1.8.6 Smart Agriculture System
Smart agriculture system incorporates GPS-based remote monitoring, moisture and temperature sensing, intruder frightening, security, leaf wetness, and suitable watering facilities. It employs wireless sensor networks to continually monitor soil characteristics and environmental conditions [116]. Throughout the farm, many sensor nodes are put at various places. These parameters are controlled by through whatever separate device or broadband Internet service, and the activities are carried out by connecting sensors, Wi-Fi, and a camera with a microcontroller. Mostly wireless sensor networks are used for agricultures system for sensing the environmental parameters such as energy consumption, topology construction and cyber-attack security system [117]. Patil and Kale [118] have used sensor technologies and an IoT wireless network to understand the current state of an agricultural system. Remote Monitoring System (RMS) is offered as a combined strategy combining online and wireless connectivity. The primary goal is to collect actual data from the farming cultivation atmosphere in order to give quick accessibility to farming amenities which includes as cautions notice via Short Messaging Service (SMS) and guidance on climate conditions, crops, and so on.
Khoa et al., [119] proposed a novel sensor node topology that emphasizes the use of low-cost, high-efficiency elements such level of water, moisture levels in soil, heat, wetness, and rainfall detectors. Furthermore, the transmission module used is based on LoRa LPWAN technology to assure effectiveness of the network. According to Naresh and Munaswamy [120], IoT modernization will aid in the collection of information on conditions such as climate, moisture, temperature, and soil productivity. Crop web-based examination enables the identification of wild plant, water level, bug location, creature disruption in the field, trim development, and horticulture. Farmers are used by IOT to connect to his home from anywhere and at any time. Remote sensor structures are used to monitor homestead conditions, while smaller scale controllers are used to regulate and mechanize house designs. Kumar et al., [121] have used an IoT-based application to monitor soil moisture utilizing a detection component, connecting through the online with Wi-Fi module, and controlling the switching of the submerged motor pump (motor driver-289D) using Arduino Uno R3.
1.9 Industry 4.0 Integrated With IoT Architecture for Incorporation of Designing and Enhanced Production Systems
Many industries have benefited from research on ICT-based production system during the last three decades, which has changed the conventional ways of designing, manufacturing, and distributing their goods [122, 123]. In industries, automated industrial operations are carried out using IoT-based systems and controls. It covers the entire process from the commencement of production through delivery of the finished product [6]. Figure 1.8 depicts the automated IoT manufacturing processes. Data from IoT-enabled manufacturing addresses connectivity, computation, and control challenges. Power stations, management of water resources, chemicals production, and materials fabrication all use IoT devices. The detectors are used to manage and regulate the production process. This could also collect actual information on the operation of industrial systems in real
Figure 1.8 IoT-based manufacturing process from [6].
time. It assists businesses in enhancing quality and regulating industrial procedures. It also is specifically designed to support the manufacturing cycle more efficiently and speed up output.
Using group technology and Industry 4.0, manufacturers are increasingly focused on genuine tracing of manufactured products in an assembly line. AGent based Systems (AGS), mobile robots, data interoperability, Digital Twins (DT’s) principles, machine learning techniques, and 3D printing are all used in Industry 4.0. Manufacturing companies face a variety of challenges when incorporating those certain technologies into their design and development phases, such as quick digital designing/redesigning of product lines, digitized recreating processes/products, production process planning and decision making, feature-based automatic machine formation, intellectual set - up/fixture planning, and supply chains/market delivery as required [123].
Kuo et al. [122] have created an algorithm that anticipate the working condition of spring manufacturing machines. They employed a multistage data collection and evaluation method to achieve Industry 4.0 workflow, in which the collected component values from processed data were anticipated using neural networks. Santos et al. [124] have developed a huge data structure model that was used at Bosch to accomplish Industry 4.0. The architecture of data gathering and analysis technologies using a big data analytics strategy, which includes web services, apps, databases, security and management. Similarly, Kumar et al., [46] have utilized Simulated Annealing (SA) centred metaheuristics and Principal Component Analysis (PCA) to propose a big data solution to the facility layout problem. It is developed a dataset with 14 criteria based on the three V’s (Volume, Variety, and Velocity) to quickly construct a defensible architecture that met their goals. Lin et al., [125] have introduced a block-chain created reciprocal recognition system with perfect accessibility of management structure for Industry 4.0. This paper provides the BSeIn conceptual framework for implementing a flexible and changeable smart factory. The study established methods for evaluating data security across terminals, the block-chain system, the manufacturing network, the cloud and tangible assets.
Likewise, Dinardo et al., [126] developed a proactive and intelligent circumstance tracking system based on regular tracking of the energizing parameters of frequency response acquired from the investigated equipment. A deeper learning method for detecting classical machining operations from CAD data was disclosed by Peddireddy et al., [127]. They adopted modest grinding and rotating properties from Standard Triangle Language (STL) models to educate their computational model for feature detection. Kim et al., [128] showed a depth restoration approach for pipe element catalogues employed in plant 3D CAD model reconstruction. In their work, they highlighted the reverse engineering concept of restructuring from 3D point cloud data/subdivided point cloud data, and they used depth learning algorithms to effectively generate the models required.
1.9.1 Five-Stage Process of IoT for Design and Manufacturing System
In a digital world, a fully networked ICT created designing and production method need a powerful AI. Furthermore, to accomplish and apply the numerous manufacturing needs, the connectivity necessitates clever fully computerized AI utilization of smart programmes that can acquire, reason and create choices on their own. For that, AI-based 5-stage design is proposed and illustrated in Figure 1.9 (taken from [129]), which represents the integration of the different technologies stated above to accomplish Industry 4.0 with IoT.
Stage 1: RFID CAD data
The proposed RFID created method in the multistage architecture addresses the difficulty of speedy development/redevelopment by merging design and manufacturing data. The issue is that it is the source of a massive quantities of data throughout the procedures, i.e., it can identify the process plan, machine status, tool paths, and availability, among other things, using product features/shapes and material (combinations) information. It will be performed in a digitally online platform and international digital CAD standards such as STEP will be required for RFID input. The concept will let us create an electronic simulator in which we will be able to apply IoT established information sharing and recovery technique to achieve Industry 4.0.
Figure 1.9 Interoperability of design and production systems in a multistage IoT-based industry 4.0 Infrastructure from the [129].
Stage 2: CAD model retrieval using web services and machine learning The next step is to obtain and display details of data from websites server in the Graphical User Interface (GUI) after scanning the RFID smart technologies with the CAD model from beginning stage. The GUI communicates with a web application which utilize the Simple Object Access Mechanism (SOAP), which is a messaging protocol for sharing structured data for retrieving CAD models. During this procedure, the user interacts with the GUI and establishes a connection among the local network computer and a distant business/web server located at a given location. The RFID CAD relevant data is saved in their separate databases.
Step 3 & 4: Agent based Computer-Aided Process Planning (ACAPP) In this study, sensors are utilized to overcome a range of complex difficulties that demand intellectual understanding and deliberate decision making, notably from stages 3 until 5 when procedure planning, toolpaths, and production schedules confirmations are necessary. Essentially, those sensors are small or ant colony size scripts that are used to solve a variety of complex problems that need advanced reasoning and better decision making. Essentially, individual sensor has four structures. The first, a belief set is a general relational model used to express agent beliefs. Second, an event is a description of an occurrence that requires the sensor to respond. Thirdly, a sensor’s plan is identical to its activities. Those are the guidelines that the agent sensors must adhere to in order to achieve its objectives and manage the happenings that have been assigned to it. Finally, a competency facilitates the pooling and reusing of an agent’s functional elements. A capacity is a collection of planned, happenings, belief sets, and other abilities that work altogether to give a sensor a specific talent.
Manufacturing generates a process plan for a basic identifiable component or shape called a “slot.” It has the ability to generate feature-based manufacturing data as well as event for manufacturing characteristics, as well as cutting edges and machine data. When such an event or aim occurs, it determines what course of action to take and achieves the procedure effectively. The ‘Plan’ will then be carried out by retrieving the particular confidence from a database of tools and process parameters, exactly like a reasonable human would.
Stage 5: CNC machine compatibility with CAD/CAM software
In a production system setting, data transfer from CAD/CAM software to a CNC machine through a CPE is tricky [130, 131]. This is due to a variety of issues faced during machining, even by expert employees. Some of them arise during decision-making and tool path adjustment in situations like tool breakage during machining; incorrect toolpath creation from a CAM application software; scarcity of cutting tool and so on. The list will get longer since machining difficulty changes depending on other parameters such as CNC machine capabilities, component form, material, and so on. In many cases, a rapid change along with CNC programme or G&M codes is necessary, although this is complex and time-consuming. This is especially true when one of these events occurs often during practical manufacturing. There are CAD/CAM software along with CNC machines to assist, however the background of the issue shifts because if a new part with a non-machinable design needs to be machined in order to ‘rapid manufacture’ the product. The current architecture considers using IoT technological innovations to solve these challenges by obtaining real-time data and determining whether or not a digitalized twin of the entire process is achieved by utilizing system applications equivalent to a Raspberry Pi, transistors for I-IoT information exchange, cloud rail box, and so on.
1.9.2 IoT Architecture for Advanced Manufacturing Technologies
RFID is a wireless transmission technique that is implemented in a variety of applications, including shipping, tracking, and delivery. RFID is a contactless technology that detects things that have smart tags attached to them. Through communication with tag antennae, an RFID reader uses previous data from goods and the environment. A wireless device domain can be used by scanners and labels [132]. RFID can also quickly distinguish a variety of products. Furthermore, its anti-collision technology allows it to differentiate a specific number of tags at the same time. The RFID framework is highly robotized and offers a broad variety of adjustments for easy and universal computing applications such as position tracking, access management, and environmental monitoring [133]. An RFID digital tag, an RFID sensor, and a digital storage are three components of an RFID system, which equipment linked to RFID that is used to record data in the cloud. The antenna and chip of an RFID smart tag are wrapped with a transmitter, controller, and polymer-encapsulating substance. As soon as the RFID scanner finds a tag, it continues to search for RFID smart tags. The data from the tag was kept in the data storage to process later [132].
Several manufacturing organizations struggle to trace components from their suppliers in order to fulfil client orders during peak time of production and fail to deliver the product on time. The present plan collects and tracks data from numerous suppliers’ factories in actual environments, such as procedure information, basic components, quality information and so on. Manufacturing business companies may manage their actual environment workspace manufacturing data includes production schedules, semi-finished goods storage, and input substances amount in process, as a result of the real-time statistics mining evaluation. The management of manufacturing organization now contains dynamic manufacturing records, which allows to analyze implemented manufacturing plan in real time, adapt quickly to changing market conditions and adjust production and purchase plans. The quality of production is handled dynamically and basic information regarding production quality and traceability can be provided.
Manufacturing organizations are started using RFID systems to provide solution to eliminate the manual coding at every level of the manufacturing process [134]. The use of an RFID built system decreases the possibility of scanner mistake in the manufacturing line. RFID tags are used by companies such as Vauxhall and BMW to customize client orders [135, 136]. A reprogrammable RFID card is configured and fastened to the production parts based on the client’s requirements. The RFID card guarantees and tracks parts for right color, interior, and any customized extra features of a specific client during the manufacturing process [137].
1.9.3 Architecture Development
For produced items, the system’s major purpose is tracking, wear estimation, quality of products, products categorization, and inventory monitoring. To identify premounted RFID tags on manufacturing items, RFIDs are connected to input module and output module in every production station, appropriately. The RFID reader recognizes the component as it passes into the location using the radio frequency identification mechanism. A vibration sensor is a component of the system that allows it to capture enormous amounts of vibrational data while drilling or milling. Smart RFID tag is a type of uniqueness that employs RF signal to detect and track smart tags [138]. The RFID based IoT architecture is adopted from [132].
System Architecture
The system architecture for gathering and analyzing data during the production process are depicted in Figure 1.10. Information passes from the smart tag to controller and followed to rear-end cloud data base, allowing for real-time analysis and monitoring of the production process while maintaining the quality of the components. The system creates a map of the production procedures and updates unique data for each item, guaranteeing that the system is transparent and traceable.
Product Tracking System
By linking RFID and vibration detecting modules to the Internet through the MQTT protocol, manufacturing parts may be tracked in real time. As follows, the system analyses production information connected to the part and stores the information to assure the final quality.
Time of arrival on the shop floor
Process start up time.
Duration of the process.
Time of departure from shop floor.
Constraints pertaining to shop floor
The manufacturing process begins with the manufacturer’s request to the product provider. As soon as the request is received, the supplier creates an RFID tag for the needed shipment and sends the part to a different production work floor. Once the component arrives at the work station, the RFID reader activates the vibrational sensor to use vibrational data to confirm the quality of the manufactured part. The RFID technology assists the controller in distinguishing each part and mapping data into the appropriate data base to improve part traceability. The system is capable of detecting the problematic part and rejecting it with the aid of a quality analysis system built in the same controller. To monitor and synchronize data, RFID tracking systems can be linked to cloud-based infrastructure.
Figure 1.10 IoT architecture [132].
Analysis of Vibration
The sensor configuration on the machine tool for recording vibration data. Tool wear assessments are used to develop and test signal processing algorithms. As a result, variations in vibration signatures may be recorded during milling operations over the tool life in order to extract a set of data that will be used as tool wear indicators.
GT-based categorization with RFID integration
