Industry 4.0 Convergence with AI, IoT, Big Data and Cloud Computing: Fundamentals, Challenges and Applications

The Internet of Things (IoT) is a network of smart and self-configuring devices that exchange data by interacting with the environment to make decisions without human intervention. Endowed to sense surrounding events, these physical objects generate large amounts of real-time data that need an acceptable architecture with better security to process and convert it into meaningful information. Implementation of blockchain in IoT offers a secure, transparent and efficient mechanism to store and manage data generated by connected IoT devices. Even though the integration of blockchain with IoT is pretty recent, there are at present a huge number of applications that include smart healthcare, smart homes, e-government, automotive industry, smart education, precision agriculture etc. However, there are several challenges encountered in Blockchain-IoT integration which include anonymity, standardization, interoperability, heterogeneity, data privacy, smart contracts, legal issues, transparency, storage capacity and scalability, security, etc. This chapter presents the current state-of-the-art Blockchain-IoT integration in order to examine how blockchain could possibly improve the IoT ecosystem catered towards Industry 4.0. This chapter investigates the various application domains of Blockchain-IoT integration. It also discusses the main challenges faced in the adoption of blockchain in IoT environments for I4.0

Challenges in the industrial world are evolving rapidly. The game-changing technologies for overcoming these challenges on the path to Industry 4.0 are digitization and automation. We can control and improve every area of supply chain and manufacturing processes with the aid of Industry 4.0 technologies. It offers access to the real-time data and insights we need to run a company more profitably and efficiently. As a result, one can make better, quicker business choices. Supply chains and manufacturing processes may be built with the use of Blockchain technology. In this chapter, the connection between Blockchain and Industry 4.0 is made clear. The nine pillars of Industry 4.0 are defined as the core value drivers of the manufacturing process. The next part provides a quick overview of the characteristics of Blockchain technology. The final section investigates the function of Blockchain in Industry 4.0 through the use of different applications. 

The manufacturing industry is revolutionized by introducing information and communication technology for productivity, flexibility, and agility. This Industry 4.0 has revolutionized the way companies manufacture, update and distribute their products. Manufacturers are integrating new technologies, like the Internet of Things (IoT), cloud computing, data analytics, and AI, into their production facilities and business operations. This digitization of manufacturing has increased the productivity of plants, reduced the dependency on human resources and improved logistics. This led to a better return on investment in the manufacturing sector and started blooming again. Many start-ups are coming up with new business ideas based on new technologies. These ideas are now easy to integrate with Industry 4.0-ready facilities. Now it is challenging to take advantage of this wave of new business opportunities for the traditional manufacturing industry. These industries strive to implement this technology for their plants, which are also located geographically remote. To sustain these older plants to new generation competition and expectations, it is necessary to shift and adopt the technology of Industry 4.0. Transition business models need to be developed for shifting to a “new business model” of the company, along with the “old business model” that is slowly “decommissioned”. In this chapter, we will discuss the need, challenges, and advantages of integrating new technology into old manufacturing processes for the sustainability of a business. Organizations that wish to shift to a new paradigm face many challenges. The most challenging aspects include the skills and qualifications of their human resource, the ability of their machines to connect and communicate with each other, and the adoption of various new technologies.

Advancement in the medical field promotes the diagnosis of disease through automation methods and prediction of the brain tumor also plays an important role due to the fact that millions of people are affected by brain tumor and the rate of affected people is increasing every year randomly. Hence, in saving the lives of many individuals, the early detection of the disease plays an important role. Using the MRI Images, it’s easy to find the location and existence of the tumor. Expert manual diagnosis is playing a vital role in detecting the information about the tumor and its type. Though there are various models that can detect tumor location with the help of ML models in the medical field, somewhere there is a lag in the success of these models. Deep learning is one of the widely used approaches for the same. But the black-box nature of these machine-learning models has somewhat limited their clinical use. Explanations are essential for users to know, trust, and well manage these models. The chapter proposes dual-weighted deep CNN classifiers for early prediction of the presence of brain tumor along with the explanation-driven DL models such as Local Interpretable Model-agnostic Explanations (LIME) and SHapley Additive explanation (SHAP). The performance and accuracy of the planned model are assessed and relate with the existing models and it is expected that it will produce high sensitivity as well as specificity. It is also expected to perform well by means of precision and accuracy.

The aim of this chapter is to focus on the status of data mining approaches for e-learning and identify the gaps in them. It is seen that a predominant work has been done in e-learning through data mining techniques such as educational recommenders, association rules, clustering, profile-based approaches, personalization, prediction techniques, and decision support systems. However limited work is done on e-learning from a cognitive science perspective, which gives us the motivation to work in this field. The proposed work aims at proposing a cognitive model for e-learning systems using data mining for Industry 4.0 applications which shall usher in a new datum for e-learning in cyber-physical systems. 

Although Usage-Based-Insurance has a wide range of applications, it is practically nonexistent in India. We have done research to help design a system that combines IoT, ML, and cloud to help us overcome this problem as a next step. The goal is to create a machine learning model for the cloud that will allow us to calculate driver and customer safety scores based on where and how the car has been driven. We were able to do this by evaluating information from our smartphone's sensors. Utilizing this data and classification system, problems like Usage-Based-Insurance can subsequently be resolved in the real world (UBI). Usage-based insurance has proven to be efficient and appealing to both insurers and the ones insured in a wide range of countries. The use of this method is sparse to nonexistent in India, but it is on the horizon. As a result, there is a lot of potential for our research and suggested works on UBI in India.

As the world is progressing more towards new technology, more and more people are getting close to computers to perform their tasks. Computers have become an integral part of life. In recent years, web-based education has been perceived as a support tool for instructors as it gives the comfort of use at any time, and any place. In this situation, recognizing the user’s engagement with the system is important to make human-computer interaction more effective. Recognizing user engagement and emotions can play a crucial role in several applications including advertising, healthcare, autonomous vehicles, and e-learning. We focus on understanding the academic emotions of students during an online learning process. Four academic emotions namely, confusion, boredom, engagement, and frustration are considered here. Based on the academic emotions of students, we can incrementally improve the learning experience. In this paper, we have developed a system for identifying and monitoring the emotions of the scholar in an online learning platform and supplying personalized feedback to reinforce the online learning process.
 To achieve this, we have extracted images from the videos of the DAiSEE dataset and performed pre-processing steps like convert it into greyscale, detect a face from that image using OpenCV, change the size of the image, and then save it. Then labeling of the emotions is done and the model is trained using a convolution neural network (CNN) on the said images. In this way, the neural network is trained and can predict the emotion.

Throughout the history of the industry, producers and retailers have focused on fruit quality as a major concern. Over the past ten years, the market for high-quality fruit has expanded quickly, raising the price of the upscale item. However, the state-of-the-art methods have some major drawbacks such as the mechanical systems are bulky, require more manpower to operate the system, are less accurate, slow, expensive and with more chances of human mistakes. In this paper, we have addressed these drawbacks and proposed systems for the management of the fruit quality and grading of fruits in different categories. These fruits can be sorted automatically depending upon their different characteristics such as color, size, shape, weight, specific gravity, sugar contents, and ph. In this paper, we have selected three characteristics of fruit namely color, size, and weight for measuring the quality of fruit and grading them accordingly. The result shows that the proposed system has successfully implemented fruit quality management and grading. The system is automatic and results in lightweight, simple and inexpensive hardware, increased speed of operation and reduces manpower, and mistakes. 

According to the English lexicon, mining is the extraction of coal or other natural minerals from a mine. Extracting these natural minerals is extremely risky, and employees' lives are at stake. Mining workers are exposed to a dangerous underground environment that can cause harm or even death. Some of these injuries or fatalities can be traced back to human error. However, several physical factors or reasons for such a subterranean environment might be blamed for these mishaps. It is not easy to monitor without endangering someone's life. Previously, companies depended on manual processes where an individual would physically inspect the situation, make observations, and submit a report. This technique was too hazardous since the person monitoring a specific threat may be harmed by the same hazard. As a result, this has been the mining industry's most serious challenge for a long time. A smart system can detect problems and communicate information to the relevant authorities before anything hazardous occurs due to this procedure. This smart network system uses wireless sensors and an IoT platform. Gas, temperature, humidity, and vibration sensors are the various types of sensors used to detect the presence of any toxic gas, monitor the temperature, identify the amount of humidity in the air, and monitor subsurface tremors, respectively. All of these sensors are linked together, and the data collected by these sensors is subsequently sent to the cloud for processing. This analysis will assist the system in understanding subsurface behavioural changes, and as a result, it will be able to provide warnings of impending dangerous circumstances. The Raspberry Pi and the Raspberries operating system are used for data analysis. This smart system intends to lower the risk of accidents and infections, benefiting workers and the company's economy.Such IoT architecture in the mining industry, which combines operational technology (OT) and information technology (IT), provides a safer mine site for workers, reliable mining operations, a highly integrated environment for both traditional and innovative sensors and equipment, automation that can reduce human intervention and covert surveillance. This research aims to increase IoT adoption in the mining sector by combining a high-level architecture, complying with all industry standards and guidelines, and addressing the mining industry's specific challenges.

Artificial Intelligence and Machine Learning are the latest topics across industries. A lot of concentration has been given to these areas and still the adoption has been challenged by users and experts in this field in the search for some kind of solution to be provided that the output can be trusted by all. The purpose of this paper is to focus on the sensor data coming from various IoT devices and how the data can be interpreted by various available algorithms. The ML algorithm is considered a black box with a focus on providing the required output without finding the causes behind the decision and working mechanism provided by that model. In this chapter, we tried to explain various common techniques/models available for eXplainable Artificial Intelligence (XAI) and how those can be used for IoT data. 

 In most nations, agriculture is the main industry providing employment. Agricultural activities used to be restricted to the cultivation of food and crops, but they have expanded over time to include the processing, production, marketing, and distribution of crops and livestock products. Agriculture related approaches or practices must be continuously reviewed with the goal of presenting innovative approaches to sustaining and improving agricultural activities. Currently, agricultural activities serve as the primary source of livelihood, increasing GDP, being one of the sources of national trade, reducing unemployment, and providing raw materials for production in other industries. Inadequate soil treatment, disease and pest infestation, among other issues, are only a few of the difficulties this industry must overcome in order to maximize productivity. There have been some difficulties with the increased use of technology in this industry, including the need for large amounts of data, low output, and the most obvious difficulty, the knowledge gap between farmers and technology. When compared to earlier more conventional methods, agricultural practices, and activities have significantly improved since technology entered the field. Technologies like the Internet of Things (IoT) and Artificial Intelligence (AI) have been a few of the technologies that are widely used in these sectors with projects for improving crop production, disease prediction, continuous monitoring, efficient supply chain management, water waste and operational efficiency just to name a few but, this of this project will focus more on AI, more specifically on Explainable Artificial Intelligence (ExAI or XAI).