The rapid growth of the Internet of Things (IoT) has led to its widespread adoption in various industries, enabling enhanced productivity and efficient services. Integrating IoT systems with existing enterprise application systems has become common practice. However, this integration necessitates reevaluating and reworking current Enterprise Architecture (EA) models and Expert Systems (ES) to accommodate IoT and cloud technologies. Enterprises must adopt a multifaceted view and automate various aspects, including operations, data management, and technology infrastructure. Machine Learning (ML) is a powerful IoT and smart automation tool within EA. Despite its potential, a need for dedicated work focuses on ML applications for IoT services and systems. With IoT being a significant field, analyzing IoT‐generated data and IoT‐based networks is crucial. Many studies have explored how ML can solve specific IoT‐related challenges. These mutually reinforcing technologies allow IoT applications to leverage sensor data for ML model improvement, leading to enhanced IoT operations and practices. Furthermore, ML techniques empower IoT systems with knowledge and enable suspicious activity detection in smart systems and objects. This survey paper conducts a comprehensive study on the role of ML in IoT applications, particularly in the domains of automation and security. It provides an in‐depth analysis of the state‐of‐the‐art ML approaches within the context of IoT, highlighting their contributions, challenges, and potential applications.

Machine learning and internet of things applications in enterprise architectures: Solutions, challenges, and open issues

Digital enterprise transformation focuses on alignment of processes, products, services, business models, and technologies to perceive business value. Digital business integration in an organization utilizes information technology and its tools to drive and manage the life cycle of digital enterprise transformation. It utilizes the practices and approaches of IT governance with modern application tools and APIs. The millennium brought many technological advancements over internet technologies and these technologies operate numerous applications and business services. The span of digital enterprises is expanding and continues to grow with their evolution on a web scale. This chapter is an effort to present understanding about machine learning and automation around businesses intelligence and analytics on a web scale. The chapter provides a brief summary of technologies used in digital enterprise transformation for all the domains of an organization.

Transforming Digital Enterprises Towards Web Scale With Machine Learning

In general, the Internet of Things (IoT) concept allows any type of electrical equipment to connect the Internet (thing). As both an outcome, any “thing” will be assigned an Internet Protocol (IP) identifier and will be reachable using normal Web protocols such as Domain Name System (DNS). The IoT will be a technology or functionality associated with conventional ISA-95 automation systems in an automated environment. IoT automation elements include sensor, actuators, Programmable Logic Controllers (PLCs), control loops, data analytics, control models, simulators, and optimizers. The IoT, Industry 4.0, and intelligent systems might all be combined to produce a strong mixture that foreshadows an amazing future with enhanced efficiency. In many areas, especially the industrial sector, long-term growth is crucial. With the help of an intelligent system, we may be able to detect and address significant issues in Industry 4.0. As a consequence, the concept may be thought of as a physical device or a feature computed in a software system that runs on any device with sufficient computing power. An IoT device presently lacks a standardized technology. Several projects to standardize are now in the works. For example, the International Telecommunication Union has a standardization working group SG20 on “IoT and its applications, including smart cities and communities,” and Institute of Electrical and Electronics Engineers (IEEE) has an ongoing IoT Ecosystem study as well as the IEEE P2413 draught standard for an architectural framework for the IoT. This chapter provides a review of the literature on revolutionary industrial growth and the difficulties and advantages of Industry 4.0. One of the most pressing issues in the current scenario is achieving long-term growth. Air quality has become a major problem on a global scale. This chapter also includes a case study on the merging of meteorological sensors with remote sensing devices. This case study demonstrates the relationship between meteorological sensors, such as temperature, wind, and relative humidity, and Atmospheric Boundary Layer (ABL) as well as its importance in regional air quality monitoring. The case study also highlights the necessity for advanced technologies such as the IoT and Artificial Intelligence (AI) to be integrated into the field of environmental monitoring. 

Fusion of Smart Meteorological Sensors, Remote Sensing Techniques, and IoT in Context of Industry 4.0

The combination of various sensors with different data methods is a common technique used to increase precision in the classification of IoT health data. However, for even the assessment outcomes, all modalities are barely available and this scarcity of evidence poses significant barriers to multimodal education. Driven by recent developments in deep education, we are providing a cross-neural network for the segmentation of the IoT Health Data Classification, which is trained on data modalities not all available during trials. In IoT Health Data Classification, we train our architecture with a cost function that is especially tailored to unbalanced classes. We are providing the device with a benchmark data set with incomplete data. Assuming that they are not present in the research process, our methodology goes beyond both the CNN training and the collection of two CNNs trained in the missing modality by utilising time data

https://turcomat.org/index.php/turkbilmat/article/download/1892/1632

Design and Implementation of Missing Data Classification Technique for IoT Applications Using Artificial Intelligence

Machine-to-machine communication is now enabled and will rule the world in the future. This is achieved through the Internet and this is called the Internet of Things (IoT) as it enables all the things in the real world to communicate with each other. IoT achieves this by bridging various other software technologies and hardware devices. IoT has flourished in all domains starting with simple home automation to businesses. As everything in this real world is business, enabling IoT in business will be of great help to the enterprises and the decision makers in the field. So here this chapter portrays what an enterprise Internet of Things deals with, its issues, and its applications. Also the importance of business forecast for an enterprise and how it is achieved via various techniques for the business forecast are discussed. For better forecasting results, the advanced machine learning algorithms that can be employed in a different perspective of enterprise IoT and their applications are presented here. This would be a great help for the researchers and practitioners in the field of enterprise IoT.

Advanced Machine Learning for Enterprise IoT Modeling

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Advanced Machine Learning for Enterprise IoT Modeling