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- Herausgeber: Bentham Science Publishers
- Kategorie: Fachliteratur
- Sprache: Englisch
Industrial Internet of Things: An Introduction explores the convergence of IoT and machine learning technologies in transforming industries and advancing economic growth. This comprehensive guide examines foundational principles, innovative applications, and real-world case studies that showcase the power of IoT-enabled intelligent systems in enhancing efficiency, sustainability, and adaptability.
The book is structured into five parts. The first part introduces industrial IoT concepts, including algorithms, deep learning prediction models, and smart production techniques. The second section addresses machine learning and collaborative technologies, focusing on artificial neural networks, and AI's role in healthcare and industrial IoT. Subsequent chapters explore real-world applications, such as IoT adoption in healthcare during COVID-19 and intelligent transportation systems. The final sections address advanced IIoT progressions and the role of IoT in energy production using byproducts.
Key Features:
- Foundational concepts and algorithms for industrial IoT.
- Integration of machine learning in IoT systems.
- Case studies on healthcare, transportation, and sustainability.
- Insights into energy production using IoT.
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Seitenzahl: 318
Veröffentlichungsjahr: 2024
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PREFACE
Machine learning approaches are highly considered in almost each application domain area. The continuous growth of computational approaches motivates the editors to work in this area. The editors worked and gather various book chapters based on the “Industrial Internet of Things: An Introduction” and selected a few chapters for this book.
The book content categorizes into various subdomains starting with an introduction to computational techniques, and the importance of computational techniques in Industrial IoT. Various challenges and issues related to computational techniques in Industrial IoT. The book also covers currently hot areas of IIoT that are mainly healthcare informatics, transportation system, etc. Case studies also related to designing and testing the IIoT frameworks were also highlighted. The book shared the implications of waste management for boosting the national economy. Some legal policies were also discussed before concluding the book.
The editors have good knowledge in the area of machine learning and deep learning techniques. The research interest of Mr. Sunil Kumar is deep learning in information analysis in the agricultural domain; Ms. Gaytri Bakshi is working in the area of deep learning approaches for Industrial information processing; Ms. Silky Goel has an interest in computer vision and deep learning techniques. Mr. Siddharth Gupta is keen interested in image processing with machine learning approaches. Mr. El-Sayed M. El-kenawy has been working in advanced ML techniques. This book is edited by these five editors with a good review process.
List of Contributors
ACKNOWLEDGEMENTS
This book is based on the flourishing aspect of IoT technology in the industrialization of the upcoming economy of India. Machine learning techniques are playing a revolutionary role in these various arenas of industry. IoT with machine learning cultivates smart industrial systems and has brought the economy, intelligence and lifestyle to yet another level. I am very grateful to have a wonderful team for encouraging each other to constantly work together and put endeavours in the correct direction. With great pride we present the book “Industrial Internet of Things: An Introduction”.
We would like to thank all the reviewers who peer reviewed all the chapters of this book. We would like to thank the editorial team of Bentham Science Publishers, for their immense support in the process of publication. Finally, we would like to thank all the authors who have contributed chapters to this book. This book would have been impossible without your efforts.
We hope that this book will enlighten a reader about both technologies, their amalgamation, their applications as well as their standardization rules into the industry sector. Moreover, it will open up doors for other researchers to come up with their own ideas. Once again, we would like to thank everyone who was a part of this effort.
DEDICATION
To all the ones who have zeal for IoT and machine learning for research and innovation.
Industrial IoT
Algorithms and Activation Function-IoT
Gaytri Bakshi1,*Abstract
In this new industrial era, IoT is an emerging technology. Industrialization has entered an entirely novel phase with the fusion or incorporation of deep neural network methods with machine learning (ML). Both sustainable living and economic prosperity have resulted from this. Predictive analysis has been both a boon to humanity and an improvement in the caliber of work produced. It has created an opportunity for people to improve society and assist the poor in numerous ways. IoT and ML integration enables humanity to create a single home on this planet.
INTRODUCTION
Engineering is an application that has taken into account the principles of maths and science to solve real-world problems. Humans have evolved technology with engineering to aspire to the next level of a smart and intelligent world. With the advent of technology, engineering has been given a new shape in terms of providing services as well as production. Engineering has dominated every industrial sector, such as civil, mechanical, automobile, chemical, electrical, electronics, computers, and instrumentation & communication. Technology in itself is an advanced version and the practical implementation of principles laid down by science. Scientific principles are embedded to create smart frameworks, which in turn develop smart systems. This has led to the fruition of the term Internet of Things, where the Internet as an architectural backbone connects everything within a system in terms of communication and actuation. IoT is an ecosystem that, enables humans to study and understand the physical environment in terms of digitization. The framework comprises sensors as the most atomic entity with a communicating protocol that connects the sensing node with the edge device and then later connects the entire system to the cloud. The received
data is analyzed by applying algorithms of AI and ML to predict or perform actuation [1] (Fig. 1).
Fig. (1)) IoT framework implementation [1].IoT architecture is a layered architecture encompassing four layers, and its service-oriented architecture in each layer has multiple functionalities, as shown in Fig. (2).
Fig. (2)) Service-Oriented Architecture of IoT.With the aim of reducing human intervention and maintaining sustainable development, IoT covers almost all the industrial sectors along with their services, as shown in Fig. (3).
Fig. (3)) IoT Service Layer.Industrial IOT version
Almost all of the industrial sector has undergone a wider transformation with the advent and implementation of IoT. It has brought an immense revolution of modernization and intelligence to every industrial sector. In the degrading environment, the concept of sustainable development has also been inculcated. The industrial sector is now heading from version 4.0 to version 5.0 with the perceptions of smart, intelligent, and innovative solutions and services. The following sectors have different aspects of adopting IoT and embedding it with machine learning (ML) algorithms to quench public demands and develop a sustainable environment.
Building Infrastructure and Home Automation
This sector deals with creating smart setups, which include public sector buildings, urban development, and smart cities. Such cities deal with sustainable living with smart things that work independently and make life secure and easy [2]. This sector even works to develop secure devices that can continuously monitor the tensile strength of walls, bridges, and buildings [3-6]. The integration of ML with IoT investigates and predicts the construction outcomes with associated materials using strength models before the beginning of construction [7].
Security
This sector includes the physical security of humans as well as gadgets or hardware. In this sector, sensors are integrated with ML and DL algorithms to detect any human infiltration either in border regions or any private property. Many aspects of the identification of human movement and its classification in many complex conditions are the major areas of research in this sector [8, 9].
Natural Disaster Management
This sector requires smart equipment to detect humans and protect them in difficult situations of natural or man-made disasters [10, 11]
Manufacturing
This sector employs smart predictive systems that continuously monitors the wearing and tearing of manufacturing units in machines and alert to repair, replace, or maintain the part and prevent the system from any disaster or complete shutdown [12, 13].
Medical and Healthcare Systems
This sector has evolved from traditional health systems to smart systems where telemedicine, intelligent and smart health applications, and smart health monitoring systems have come into existence. With the incorporation of IoT, ML and deep learning (DL) models, predictive analysis of many diseases is done so as to take precautionary measures. Another aspect of this sector is to maintain health records with a smart system [14].
Environmental Monitoring
This sector deals with protecting and maintaining the environment. With the integration of both IOT and ML, the changing environment is continuously monitored and studied by many environmentalists [15, 16]. Protecting biodiversity and studying wildlife’s habitat are also the aspects of this sector.
Energy Management
This is another sector where energy development harnessed from natural sources of sun, water waves, air, pressure, and metals is emphasized. Building a smart system is one of the attractive aspects of this sector [17]. This sector includes electricity distribution, maintenance, and surveillance [18]. A smart grid is one of the newest innovations in this sector, incorporating both IoT and ML.
Transportation
This sector includes many applications such as traffic routing and maintenance [19], smart parking, and travel tracking by air and trains. Another sector, named logistics, is fully dependent on smart transportation. Smart toll tax collection is one of the applications of this sector. The entire concept of smart transportation and smart roads can be implemented using telematics. Prevention of accidents in adverse climatic conditions is also one of the research areas of this sector.
Agriculture
This sector focuses on the smart way of cultivating crops. Many types of sustainable automatic agriculture methods have been adopted using IOT principles [20]. The study of agriculture covers topics such as plant care, crop and production management, soil care, disease care, weed care, water care, animal tracking, etc. By applying ML to sensor data and artificial intelligence system applications that offer more suggestions for decision-making, whole farm management can be further enhanced. Adopting ANN-based ML algorithms helps improve plant management systems [21].
Algorithms used in the IoT sSctor
Traditional Algorithms for IoT Applications
Support Vector Machine (SVM)
It is a supervised ML algorithm that is widely used for categorization and regression tasks. The benefits of SVM include:
Effective in high-dimensional spaces.Robust to overfitting.Versatile and flexible.Effective in small sample sizes.Handles outliers well.Theoretical guarantees.Works well with both linear and non-linear data.It is worth noting that SVMs may have some limitations as well, such as the need to choose appropriate kernel functions and tune hyperparameters. Additionally, SVMs can be statistically pricey for copious datasets, particularly when using non-linear kernels. However, overall, SVMs are a robust and conventional machine learning algorithm with several benefits in various domains. The decision function for a linearly separable issue with three samples along the border edges—referred to as “support vectors”—in h-dimensional spaces is seen in Fig. (4).
Fig. (4)) Decision function for a linearly separable problem using SVM.K-Nearest Neighbours (KNN)
It is a popular machine learning algorithm used for both categorization and regression tasks. Here are some benefits of KNN:
Simple and intuitive.Non-parametric.No training phase.Flexibility in decision boundaries.Robust to noisy data.Adaptability to new data.Versatility in distance metrics.Interpretable results.Despite its benefits, KNN also has some considerations. It can be numerically high-priced, specifically when applied to enormous datasets, as mathematically, the algorithm evaluates gaps between all training instances. Furthermore, determining the optimal rate of k and selecting the appropriate distance metric are vital concerns for achieving good performance with KNN. Overall, KNN is a flexible and intuitive algorithm that can be effective in various scenarios, particularly when the dataset is not large and the decision boundaries are complex or not well-defined.
Fig. (5) shows cases of two distinct categories, namely Category A and Category B, and with the upcoming new data point, the classification issue arises. To solve such issues, the K-NN algorithm is used.
Fig. (5)) K-NN working for class classification..Linear Discriminant Analysis (LDA)
Linear discriminant analysis (LDA) is a dimensionality reduction technique and a classification algorithm. It is commonly used for feature extraction and pattern recognition tasks. Here are some key points and benefits of linear discriminant analysis:
Feature extraction and dimensionality reduction.Supervised learning.Assumes Gaussian distributions.Effective with small sample sizes.Computationally efficient.Interpretable results.Can be combined with other classifiers.While LDA offers several benefits, it is worth noting that LDA assumes that the data follows Gaussian distributions and that the covariance matrices are equal. These assumptions may not hold in all scenarios. Additionally, LDA is a linear technique and may not be suitable for datasets with complex non-linear relationships. LDA is a powerful technique for feature extraction, dimensionality reduction, and classification tasks, particularly when the class separability is well-defined and the assumptions of the algorithm are met. Fig. (6) depicts the different criteria for developing a new axis.
Fig. (6)) Conversion of 2D plane into a 1D plane using LDA.Naive Bayes (NB)
It is a machine learning algorithm based on the likeliness that is commonly used for categorization tasks. This algorithm formation is based on the principles of Bayes' theorem and supposes autonomy between features. Here are some key points and benefits of Naive Bayes:
Simple and fast.Probabilistic framework.Independence assumption.Handles high-dimensional data.Works well with small training sets.Easy to interpret.Handles categorical and numerical features.Incremental learning.While NB has several benefits, it is important to consider that the independence assumption may not always hold in real-world data. In such cases, other algorithms that can capture dependencies between features may be more appropriate. Additionally, NB can be sensitive to the presence of irrelevant features, as the independence assumption assumes that all features are equally informative.
NB is a simple and competent algorithm that is broadly operated for tasks dealing with categorization into a number of classes, especially in situations where the unconstrained hypothesis accommodates well or while working with high-dimensional data.
Random Forest
It is a collective learning methodology that amalgamates the predictions of compound decision trees to form an accurate prediction. It is widely used for both grouping and regression tasks. Here are some key points and benefits of random forest:
Ensemble learning.Decision tree-based.Reduction of overfitting.Feature importance estimation.Robust to outliers and missing data.Handles high-dimensional data.Less prone to overfitting.Scalable and parallelizable.Versatile and flexible.While RF has numerous pros, there is an urgency to state that it might not be the most reliable choice for all scenarios. For instance, if interpretability is a critical factor, the individual decision trees within the random forest are more interpretable than the ensemble itself. Additionally, RF may not perform well on datasets with high levels of noise or heavily imbalanced classes.
Overall, RF is a powerful and widely used composite learning methodology that addresses the limitations of individual decision trees, providing improved prediction exactness and robustness.
Advanced Algorithms for IoT Applications
Artificial Neural Networks (ANNs)
This term is inspired by the biological neuron and its network, which eventually forms the entire human brain. These networks feature the interconnection of neurons at numerous levels. These artificial neurons are termed as nodes. These networks of neurons are motivated using methodologies of genetic algorithms, providing the ability to a machine to grasp the features or information and give a decision analogous to the human brain. The artificial neuron is represented in Fig. (7).
Fig. (7)) Artificial Neuron.DL
Artificial neural networks are the superset, while deep learning is its subset. These networks are illumed by the organization and maneuvering of the human brain. Deep learning is one of the vital constituents of subjects such as statistical and predictive computation and analysis. This helps with faster calculations and critical analysis of substantial volumes of data. With the commencement of deep learning in the era of analytics, predictions have been automated. Deep learning methodologies have outperformed conventional ML algorithms. It is a highly preferred network to be employed in areas such as recognition through images, recognition through speech, reproduction of enhanced images, and classification or categorization of objects. Feedforward neural networks, convolutional neural networks (CNNs), and recurrent neural networks(RNNs) are the three most popular DL architectures [22]. These models are known as deep neural networks because these models apply the concept of hidden layers, which are actually networks of neurons. A deep network can have around 150 hidden layers. The DL layer architecture is shown in Fig. (8).
Fig. (8)) Deep neural network architecture.CNN
It stands for convolution neural network and is one of the DL approaches. Models based on this approach work on determining different objects by learning about weights and biases. These models are analogous to the neuron connectivity in the human brain. The entire working of this methodology is represented in Fig. (9).
Conv_1Convolution Max pooling Conv_2 Convolution Max pooling Fully Connected Relu Activation Fully Connected Neural Network.
Fig. (9)) Block diagram of the architecture of CNN.CONCLUSION
IoT is an upcoming technology in this new era of the industrial world. With the integration of ML and deep neural network algorithms, industrialization has stepped into a new version of 5.0. This has led to economic growth as well as sustainable living. Predictive analysis has marked an upgraded standard of work performed as well as a helping hand for the human race. It has generated a scope where humans can uplift society and help the underprivileged in many aspects. Integrating IoT with ML helps the human race make this world one home.
REFERENCES
[1]Bakshi G.. , ; IoT Architecture Vulnerabilities and Security Measures.. In: Bhardwaj A., Sapra V., editors. Security Incidents & Response Against Cyber Attacks. EAI/Springer Innovations in Communication and Computing.. Cham: Springer; 2021.[2]Somani S., Solunke P., Oke S., Medhi P., Laturkar P.P.. IoT based smart security and home automation., 2018 Fourth International Conference on Computing Communication Control and Automation (ICCUBEA).2018: 1-4. [CrossRef][3]Xakimovich A. S.. Analyzing the Results of Monitoring the Situations that May Occur in Emergency Situations of Bridges Through Various Optical Sensors., Global Scientific Review.2022; 8: 80-88.[4]Kim W.K., Kim J., Park J., Kim J.W., Park S.. Verification of Tensile Force Estimation Method for Temporary Steel Rods of FCM Bridges Based on Area of Magnetic Hysteresis Curve Using Embedded Elasto-Magnetic Sensor., Sensors (Basel).2022; 22(3): 1005. [CrossRef] [PubMed][5]Zhang W., Gao J., Shi B., Cui H., Zhu H.. Health monitoring of rehabilitated concrete bridges using distributed optical fiber sensing., Comput. Aided Civ. Infrastruct. Eng..2006; 21(6): 411-424. [CrossRef][6]Kim J., Kim J.W., Lee C., Park S.. Development of embedded EM sensors for estimating tensile forces of PSC girder bridges., Sensors (Basel).2017; 17(9): 1989. [CrossRef] [PubMed][7]Kim J., Park S.. Field applicability of a machine learning–based tensile force estimation for pre-stressed concrete bridges using an embedded elasto-magnetic sensor., Struct. Health Monit..2020; 19(1): 281-292. [CrossRef][8]Bakshi G., Aggarwal A.. Non-Invasive Methods of Detecting Human Using Computer Vision by Incorporating Machine-Learning Techniques in Open Environment at Diverse Viewpoints., 10th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions) (ICRITO).2022: 1-5. [CrossRef][9]Bakshi G., Aggarwal A.. Computational Approaches to Detect Human in Multifaceted Environmental Conditions Using Computer Vision and Machine Intelligence– A review., 2022 Third International Conference on Intelligent Computing Instrumentation and Control Technologies (ICICICT).2022: 1547-1554. [CrossRef][10]Pandey P., Litoriya R.. Elderly care through unusual behavior detection: a disaster management approach using IoT and intelligence., IBM J. Res. Develop..2019; 64(1/2): 15-1.[11]Kamruzzaman M.D., Sarkar N.I., Gutierrez J., Ray S.K.. A study of IoT-based post-disaster management., International Conference On Information Networking (ICOIN).2017: 406-410. [CrossRef][12]Nangia S., Makkar S., Hassan R.. IoT based predictive maintenance in manufacturing sector., Proceedings of the International Conference on Innovative Computing & Communications (ICICC).. 2020[CrossRef][13]Badarinath R., Prabhu V.V.. Advances in internet of things (IoT) in manufacturing, Advances in Production Management Systems. The Path to Intelligent, Collaborative and Sustainable Manufacturing: IFIP WG 5.7 International Conference, APMS 2017, Hamburg, Germany, September 3-7, 2017, Proceedings, Part I .2017: 111-118. Springer International Publishing.;[14]Bakshi G., Aggarwal A., Sahu D., Baranwal R.R., Dhall G., Kapoor M.. Age, Gender, and Gesture Classification Using Open-Source Computer Vision. In Emerging Technologies in Data Mining and Information Security., Proceedings of IEMIS.2022; 2: 63-73. [Singapore: Springer Nature Singapore.].[15]GRARI. Using IoT and ML for Forest Fire Detection, Monitoring, and Prediction: a Literature Review., J. Theor. Appl. Inf. Technol..2022; 100(19)[16]Shivaprakash K.N., Swami N., Mysorekar S., Arora R., Gangadharan A., Vohra K., Jadeyegowda M., Kiesecker J.M.. Potential for Artificial Intelligence (AI) and Machine Learning (ML) applications in biodiversity conservation, managing forests, and related services in India., Sustainability (Basel).2022; 14(12): 7154. [CrossRef][17]Bedi P., Goyal S. B., Rajawat A. S., Shaw R. N., Ghosh A.. Application of AI/IoT for Smart Renewable Energy Management in Smart Cities, AI and IoT for Smart City Application.2022: 115-138.[18]Sarmas E., Spiliotis E., Marinakis V., Tzanes G., Kaldellis J.K., Doukas H.. ML-based energy management of water pumping systems for the application of peak shaving in small-scale islands., Sustain Cities Soc..2022; 82: 103873. [CrossRef][19]Bakshi G., Kumar R., Rajnikanth U.. A Smart Approach to Detect Helmet in Surveillance by Amalgamation of IoT and Machine Learning Principles to Seize a Traffic Offender., Proceedings of the International Conference on Paradigms of Computing, Communication and Data Sciences: PCCDS 2020.2021: 701-715.[20]Gondchawar N., Kawitkar R.S.. IoT based smart agriculture., Int. J. Adv. Res. Comput. Commun. Eng..2016; 5(6): 838-842.[21]Gandhi R.R., Chellam J.A.I., Prabhu T.N., Kathirvel C., Sivaramkrishnan M., Ramkumar M.S.. Machine Learning Approaches for Smart Agriculture., 6th International Conference on Computing Methodologies and Communication (ICCMC).2022: 1054-1058.[22]Thennavan S., Gokul D., Jayapalan A.. Deep Learning Based Fake Stamp Detection., 2023 International Conference on Computer Communication and Informatics (ICCCI).2023: 1-4.Deep Learning-Based Prediction Model for Industrial IoT: An Assured Growth
Tuhina Thapliyal1,*Abstract
Industry 5.0 is a revolutionary change for the traditional industrial domain with an amalgamation of interactive computational techniques. However, the Industrial Internet of Things (IIoT) is referred to as communication between various battery-enabled physical devices. The present IIoT sector faces issues like complex decision-making, enhancement of productivity capabilities, management of the cost of assets, uninterrupted connectivity, and security. Traditional computational techniques were partially successful in finding an appropriate solution for existing issues in IIoT. In this study, the author highlighted a deep learning-based prediction model that further assists the industry while making major decisions. This approach is currently used for various problems in agriculture, healthcare, coal and petroleum, entertainment and sports, surveillance, and retail and marketing industries.
INTRODUCTION
The term “Industrial Revolution” implies adopting smart ways and features that can change the workflow of traditional industries. The Industrial Revolution, which began in the late 18th century (1760-1840) with the invention of machinery such as steam engines, marked a significant milestone in human achievement. The introduction of the first weaving loom in 1784 led to the emergence of various small industries catering to both individual clients and large organizations [1]. This digital transformation, encompassing IIoT stakeholders and the application of Industry 4.0 and 5.0, has captured the attention of industry owners, spurring increased investment in the IIoT market. The market size grew to approximately $124 billion in 2021 and is poised for further growth this year [2, 3]. Such an increase in IIoT infrastructure set up by the industries further requires an intelli-
gent data analysis system so that the streams of data generated by the IIoT devices can be processed and analyzed to derive the information that is important for the industry’s growth.
As Fig. (1) depicts, deep learning (DL) is a division of machine learning (ML) and artificial intelligence (AI) [4]. Both machine learning (ML) and deep learning (DL) are derived from artificial neural networks (ANN), which are key technologies that form the basis of the Fourth Industrial Revolution (Industry 4.0) [5].
Fig. (1)) Layers of computational techniques.
