Towards a JSON-Serialization for AutomationML

Logic synthesis is an important step in process design and automation. Electronics witnessed tremendous improvements in the reliability of automatically designed circuits with reduced lead time with the application of logic synthesis in circuit design. Industrial process automation systems using hydraulic and pneumatic circuits need logic synthesis to obtain proper sequence of process operations. Deterministic approaches have been used for decades to aid in logic synthesis. These approaches are limited by the volume of data that could be handled because of phenomena like exponential explosion. This paper presents an approach based on hybrid intelligence for logic synthesis for industrial automation systems using pneumatic sequential circuits. The proposed logic synthesis system starts with the sequence of operations as its input and generates the truth table required for the pneumatic circuit for performing the given set of operations. The proposed system hybridizes rule based logic programming and genetic algorithms and is capable of solving considerably difficult problems through an evolutionary strategy. Case studies are presented to demonstrate the capability of the proposed approach for logic design for pneumatics based industrial automation.

The integration of Artificial Intelligence (AI) and Machine Learning (ML) in industrial automation and cybersecurity has become increasingly vital as industries transition towards smarter and more efficient systems. With the rapid advancement of technology, AI and ML are playing a key role in optimizing operations, enhancing safety, and improving overall system performance. This paper explores the applications of AI and ML in industrial automation, focusing on their transformative impact on manufacturing, process control, and predictive maintenance. Additionally, the paper highlights the critical role these technologies play in enhancing cybersecurity frameworks, particularly in the context of industrial control systems and critical infrastructure. The combination of AI and ML allows for real-time decision-making, anomaly detection, and the mitigation of security risks in an increasingly interconnected industrial environment. The findings suggest that AI and ML are not only optimizing operational efficiency but also providing advanced threat detection mechanisms to combat sophisticated cyber threats. This paper presents a detailed review of these technologies' capabilities and challenges, offering insights into their future potential in industrial settings.

This article describes the development and implementation of a methodology for evaluating the effectiveness of model data exchanges between Systems Engineering tools. The context of the work is the SEDRES (System Engineering Data Representation and Exchange Standardization) project. This is a European initiative toward the development of a standardized data exchange capability. The paper addresses the issues of the use of this capability, and its potential influence on the System Engineering process. It describes the development of the evaluation framework and methods that were tested and refined for the SEDRES model data exchange context. Results that demonstrate the potential benefits of adopting the standards-based approach are described and a number of issues relevant to its applicability in the support of integrated product teams are discussed. © 2001 John Wiley & Sons, Inc. Syst Eng 4: 13–23, 2001

Industry 4.0 refers to the current trend of automation and data exchange in manufacturing and other industries. AI, or artificial intelligence, plays a significant role in Industry 4.0 by enabling the development of intelligent machines that can work alongside humans and optimize industrial processes. They are many ways in which AI is impacting the global economy in the context of Industry 4.0: Improved Efficiency: AI-powered machines are capable of analyzing large amounts of data and identifying patterns that are beyond human capabilities. This enables them to optimize production processes, reduce waste, and improve quality control. AI can enable businesses to develop new revenue streams and business models by providing insights into customer preferences and behaviors and enabling the creation of new products and services. AI is expected to drive economic growth by improving productivity and efficiency, creating new industries and jobs, and enabling businesses to expand into new markets. The role of AI in Industry 4.0 is significant, and it is transforming the global economy in many ways. As AI continues to evolve, it will play an increasingly significant role in shaping the future of manufacturing and other industries. This review paper through adequate light on the major influences of AI on the world economy and its possible implication in India. This paper will be based on the extensive literature review of previous papers and finally drawn conclusion.

This study aims to analyze the restaurant green accounting policy in restaurants in Samosir Regency, Lake Toba, in supporting sustainable tourism. Green accounting plays an important role in recording and reporting environmental aspects in the restaurant business, which is an integral part of the tourism ecosystem. This study examines the influence of Corporate Social Responsibility (CSR), management support, and community support on the implementation of green accounting in these restaurants. Using a mix method approach (qualitative and quantitative), data were collected from 71 restaurants in the area. The results showed that CSR, management support, and community support have a significant influence on the implementation of green accounting , which ultimately supports sustainable tourism in Samosir Regency. This study provides important insights for restaurant business actors and stakeholders in designing environmentally friendly and sustainable tourism policies.

Industry 4.0, a subset of the fourth industrial revolution, derives industrial automation for manufacturing sectors by setting targets and mechanisms for industrial data exchange. 5G system is considered as a communication platform that provides flexibility and wireless connectivity to support industrial automation. However, several issues are to be addressed in order for 5G NR to enable industrial automation, such as, efficient multiplexing between industrial flows i.e. maintaining QoS for critical traffic while enhancing efficiency for other traffic. In this paper, we present our view on how to solve several relevant issues, from medium access control (MAC) layer perspective. The main advantage of our solutions is that it requires very low complexity resulting in minimal changes to existing standardization specification.

The arrival of 5G technology presents substantial opportunities for industrial automation through enhanced connectivity and real-time data exchange. This integration enables seamless communication among Internet of Things (IoT) devices, heralding a new era of smart manufacturing characterized by increased productivity, reduced downtime, and improved decision-making. This study explores the synergy between 5G and IoT in optimizing industrial processes, emphasizing critical areas such as massive machine-type communications (mMTC), ultra-reliable low-latency communication (URLLC), and edge computing capabilities. The integration of the Internet of Things (IoT) with 5G technology is revolutionizing industrial automation by offering unprecedented levels of connectivity and real-time data sharing. With its support for massive machine-type communications (mMTC) and ultra-reliable low-latency communication (URLLC), 5G facilitates the seamless operation of IoT devices, enabling applications such as autonomous systems, remote monitoring, and predictive maintenance. This convergence enhances data analytics, leading to more informed decision-making and improved operational efficiency while reducing downtime. However, challenges such as security vulnerabilities, interoperability issues, and the need for robust infrastructure must be addressed. As more industries embrace this transformative technology, the combination of 5G and IoT is poised to enhance agility, scalability, and sustainability in the digital age.

This book presents cutting-edge emerging technologies and approaches in the areas of service-oriented architectures, intelligent devices and cloud-based cyber-physical systems. It provides a clear view on their applicability to the management and automation of manufacturing and process industries. It offers a holistic view of future industrial cyber-physical systems and their industrial usage and also depicts technologies and architectures as well as a migration approach and engineering tools based on these. By providing a careful balance between the theory and the practical aspects, this book has been authored by several experts from academia and industry, thereby offering a valuable understanding of the vision, the domain, the processes and the results of the research. It has several illustrations and tables to clearly exemplify the concepts and results examined in the text and these are supported by four real-life case-studies. We are witnessing rapid advances in the industrial automation, mainly driven by business needs towards agility and supported by new disruptive advances both on the software and hardware side, as well as the cross-fertilization of concepts and the amalgamation of information and communication technology-driven approaches in traditional industrial automation and control systems. This book is intended for technology managers, application designers, solution developers, engineers working in industry, as well as researchers, undergraduate and graduate students of industrial automation, industrial informatics and production engineering.

Presently, Large communication energy is required for the entire project planning system in automation industries. This paper suggests the loss of energy efficiency modeling based on the Multiple Fuzzy Aggravated Energy Scheduling Approach (MFAESA). The Multiple Fuzzy Algorithms Energy Scheduling Approach in assistance with IoT setting builds up and incorporates the fuzzy algorithm in a single objective energy loss problem, based on the preparation period for energy savings and facilities. The algorithm searches the network's idling time and optimizes the task of preparing the energy usage approach to reduce the IoT system's overall energy usage in automation industries. The results show that Multi-Fuzzy Algorithms Energy Scheming outperforms conventional system design which improves accuracy and reduces the execution time.

Industrial automation and control systems (IACS) are tremendously employing supervisory control and data acquisition (SCADA) network. However, their integration into IACS is vulnerable to various cyber-attacks. In this article, we first present population extremal optimization (PEO)-based deep belief network detection method (PEO-DBN) to detect the cyber-attacks of SCADA-based IACS. In PEO-DBN method, PEO algorithm is employed to determine the DBN's parameters, including number of hidden units and the size of mini-batch and learning rate, as there is no clear knowledge to set these parameters. Then, to enhance the performance of single method for cyber-attacks detection, the ensemble learning scheme is introduced for aggregation of the proposed PEO-DBN method, called EnPEO-DBN. The proposed detection methods are evaluated on gas pipeline system dataset and water storage tank system dataset from SCADA network traffic by comparing with some existing methods. Through performance analysis, simulation results show the superiority of PEO-DBN and EnPEO-DBN.

This paper discusses the new auspicious trends of internet of things (IoT) and its advanced developments in various sectors of today’s IT arena, including for industrial sectors. Each object acts as a communication node (or entity) that is able to communicate with other nodes, and corresponding information could be accessed and controlled, via several of today’s commonly used electronic devices such as laptops and cellular devices, etc., through an IoT-designed platform. Moreover, in industrial automation sectors, the employment of IoT is very advantageous, in terms of remote communication support; low-cost operations and maintenance; and autonomous collaborations among remote networked field devices, etc. The facility of autonomous collaborations provisioned by IoT, in the context of a supervisory control and data acquisition (SCADA) system as part of an industrial control system, somehow exists in a distributed network protocol (DNP3) during automation and controls. Therefore, the current study takes the step to model a new IoT framework for a SCADA system, which will be efficient at facilitating industrial automation through the collaborative DNP3-Modbus acquisitions and automation, called the SCADA-IoT system. An IoT gateway is employed and configured that supports for both SCADA protocols, such as DNP3 and Modbus and is efficient at communicating, from networked field devices, with inter-processing from both. In the overall SCADA-IoT design, the transmission is carried from an enormous amount of sensors and/or field devices, employing proprietary and nonproprietary protocols; further, sent information is analyzed via big data, stored in a cloud center, monitored and controlled over a SCADA-IoT supportive platform. At the same time, information security (IS) is a big associated challenge and one of the main contributions of this study. Thus, this study also analyzed the potential security mechanisms for securing SCADA-IoT and found cryptography to be a noteworthy security solution, based on the proposed system requirements and its communication demands.

In the IT world, hybrid cloud architectures have become integral to digital transformation strategies. According to Flexera's 2024 State of the Cloud Report, 94% of enterprises have adopted cloud services, with 39% implementing hybrid cloud models to balance on-premises infrastructure with public cloud platforms1. This shift introduces complex data integration challenges, particularly when integrating mission-critical systems like Salesforce. Salesforce maintains a dominant position in the Customer Relationship Management (CRM) market, holding a 21.7% market share as of 2024. Its role as a central hub for customer data and business process automation underscores the necessity for efficient and secure data integration across hybrid environments. Traditional data integration methods, such as Extract-Transform-Load (ETL) processes, often result in latency, increased operational costs, and data duplication risks. These issues are exacerbated when real-time data access is required across disparate systems2. Zero-copy integration emerges as a compelling solution to these limitations. By enabling real-time, read-only access to source systems without data duplication, zero-copy architectures facilitate seamless interaction between Salesforce and on-premises systems. Techniques such as data virtualization, federated queries, and external object modeling allow for efficient data exchange, reducing latency and maintaining control over sensitive information. Salesforce's Data Cloud exemplifies this approach, offering zero-copy capabilities that harmonize data across sources via metadata, eliminating the need for data duplication or migration3. This whitepaper explores the implementation of zero-copy integration strategies to modernize hybrid cloud data exchange for Salesforce. It addresses security, scalability, and performance considerations, providing architectural best practices and technology options to support enterprise adoption of zero-copy methods in hybrid environments.

Pandemic has showcased to the world the need for having seamless health data exchange. One of the major reasons for India’s success in vaccinating her vast population is use of digital technology to ensure an easy mechanism for the people to register for vaccinations, get appointment dates as well as the jabs itself. This ease of use has made even the rural population appreciate the need for seamless data exchange to provide excellent health care. Keep this viewpoint in mind, the Indian government has chosen to usher in unique health id for each person based on their KYC / Aadhar card number. This is a welcome change albeit riddled with privacy concerns. In this article the author seeks to analyze the strength of digital health data framework upon the touchstone of privacy and informed consent. The author seeks to find out if the existing policies and laws are strong enough to ally privacy concerns or if there is an emergent need to ensure that unique digital health id does not result in data and privacy breaches across the digital health data framework. The author also seeks to find out if the data policies of the government are followed in practice at the ground level, wherein the people might be unaware of ‘informed consent.’

The Internet of Things (IoT) has become a paradigm shifter, connecting an enormous number of smart devices and facilitating seamless data exchange for a diverse array of applications. The availability and effective use of the IoT ecosystem's resources are key factors in determining how its practical applications will develop as they mature. The IoT resources and their practical application across several areas are thoroughly explored in this paper. The paper begins by classifying and describing the various sensor types, their applications in various fields, and IoT resources, highlighting their contributions to real-time data collection, processing, and transmission. It then goes on to demonstrate a wide range of real-world uses for these resources, such as smart cities, education, agriculture, business, healthcare, environment monitoring, transportation, and industrial automation. However, utilizing IoT resources effectively is not without difficulties. Critical difficulties such as resource allocation, scalability, security, interoperability, and privacy concerns are identified and discussed in the paper. Furthermore, the paper also highlights future directions and emerging trends in IoT resource management, including edge computing, cloud computing, human machine integration, and compatibility with other systems. These developments aim to increase the dependability of IoT applications in diverse settings and optimize resource allocation. This paper's conclusion highlights the crucial role that IoT resources play in advancing real-world applications across a variety of areas. Researchers, practitioners, policymakers, and other stakeholders may collaborate together to effectively leverage the full potential of IoT resources to build intelligent, effective ecosystems that meet the needs of contemporary society by solving difficulties and utilizing developing trends.

Abstract: This chapter delves into the transformative alliance between Artificial Intelligence (AI) and the Internet of Things (IoT), heralding the dawn of smart ecosystems. These ecosystems represent a paradigm shift in technology, where the seamless integration of AI's decision-making capabilities with IoT's connectivity fabric creates environments that are not only interconnected but also intelligent. By exploring the foundational technologies underpinning smart ecosystems, their revolutionary applications across sectors such as smart homes, industrial automation, healthcare, and urban planning are examined. Furthermore, the narrative navigates the challenges inherent in realizing these systems, including security, privacy, and ethical considerations, while also contemplating their far-reaching implications for future innovation. The vision for the future outlined herein emphasizes autonomous ecosystems, enhanced human-technology interactions, and the global impact of AI-IoT technologies. Through this exploration, the chapter underscores the potential of the AI-IoT alliance to redefine our interaction with technology and its capacity to address some of the most pressing global challenges. Keywords/Index Terms: Smart Ecosystems,Artificial Intelligence (AI),Internet of Things (IoT),AI-IoT Integration,Autonomous Systems,Human-Technology Interaction,Smart Homes,Industrial Automation,Smart Healthcare,Smart Cities,Security and Privacy in AI-IoT,Ethical AI,Sustainable Innovation and Global Impact.