Edge AI for Industry 4.0: An Internet of Things Approach

In this paper, we study the edge artificial intelligence (AI) techniques for industry 4.0. More specifically, we assume fog computing takes place on the edge of Industrial Internet of Things (IIoT) networks. We provide details about the three main edge AI techniques that can contribute to the future industrial applications. In particular, we deal with the active learning (AL), transfer learning (TL) and federated learning (FL), where AL is used to deal with the problem of unlabeled data, the TL is used to start training with a pre-trained model and the FL is a distributed solution to provide privacy. Finally, their combination is developed too that we name it federated active transfer learning (FATL). Simulation results are carried out that reveal the gain of each solution and their FATL combination. The deployment of FATL in IIoT networking standards such as IEEE P2805 is described too that can be extended as our future work

Application of Artificial Intelligence in Automation of Supply Chain Management

A well-functioning supply chain is a key to success for every business entity. Having an accurate projection on inventory offers a substantial competitive advantage. There are many internal factors like product introductions, distribution network expansion; and external factors such as weather, extreme seasonality, and changes in customer perception or media coverage that affects the performance of the supply chain. In recent years Artificial Intelligence (AI) has been proved to become an extension of our brain, expanding our cognitive abilities to levels that we never thought would be possible. Though many believe AI will replace humans, it is not true, rather it will help us to unleash our true strategic and creative potential. AI consists of a set of computational technologies developed to sense, learn, reason, and act appropriately. With the technological advancement in mobile computing, the capacity to store huge data on the internet, cloud-based machine learning and information processing algorithms etc. AI has been integrated into many sectors of business and been proved to reduce costs, increase revenue, and enhance asset utilization. AI is helping businesses to get almost 100% accurate projection and forecast the customer demand, optimizing their R&D and increase manufacturing with lower cost and higher quality, helping them in the promotion (identifying target customers, demography, defining the price, and designing the right message, etc.) and providing their customers a better experience. These four areas of value creation are extremely important for gaining competitive advantage. Supply-chain leaders use AI-powered technologies to a) make efficient designs to eliminate waste b) real-time monitoring and error-free production and c) facilitate lower process cycle times. These processes are crucial in bringing Innovation faster to the market

Cognitive Digital Twins for Smart Manufacturing

Smart manufacturing or Industry 4.0, a trend initiated a decade ago, aims to revolutionize traditional manufacturing using technology-driven approaches. Modern digital technologies such as the Industrial Internet of Things (IIoT), Big Data Analytics, Augmented/Virtual Reality, and Artificial Intelligence (AI) are the key enablers of new smart manufacturing approaches. The digital twin is an emerging concept whereby a digital replica can be built of any physical object. Digital twins are becoming mainstream; many organizations have started to rely on digital twins to monitor, analyze, and simulate physical assets and processes. The current use of digital twins for smart manufacturing is largely limited to (i) status monitoring, (ii) simulation, and (iii) visualization. For status monitoring, digital replicas of physical assets (e.g., machines) are created, machines are continuously monitored using IIoTs, and the latest status of a machine can be assessed by querying its digital twin. For simulation, digital twins of machines, processes, and products are created to mimic real settings. Simulation allows the design, development, and testing of new products and processes using their digital twins before applying them to actual physical assets, this is presented in. For visualization, digital twins can include real-time dashboards and alert systems to monitor and debug an operational environment. However, in contemporary cases, digital twins are simply considered to be an exact replica of the physical assets, without any value-added services built on top of them which could convert physical assets into autonomous intelligent agents. A major advantage of this enhanced design of digital twins is that they can offer much more than just an exact replica to support value-added services on top of digital twins, which are not possible on the physical assets

Linked data as medium for distributed Multi-Agent Systems

The conceptual design and discussion of multi-agents systems (MAS) typically focuses on agents and their models, and the elements and effects in the environment which they perceive. This view, however, leaves out potential pitfalls in the later implementation of the system that may stem from limitations in data models, interfaces, or protocols by which agents and environments exchange information. By today, the research community agrees that for this, that the environment should be understood as well as abstraction layer by which agents access, interpret, and modify elements within the environment. This, however, blurs the the line of the environment being the sum of interactive elements and phenomena perceivable by agents, and the underlying technology by which this information and interactions are offered to agents. This thesis proposes as remedy to consider as third component of multi agent systems, besides agents and environments, the digital medium by which the environment is provided to agents. "Medium" then refers to exactly this technological component via which environment data is published interactively towards the agents, and via which agents perceive, interpret, and finally, modify the underlying environment data. Furthermore, this thesis will detail how MAS may use capabilities of a properly chosen medium to achieve coordinating system behaviors. A suitable candidate technology for digital agent media comes from the Semantic Web in form of Linked Data. In addition to conceptual discussions about the notions of digital agent media, this thesis will provide in detail a specification of a Linked Data agent medium, and detail on means to implement MAS around Linked Data media technologies.Sowohl der konzeptuelle Entwurf von, als auch die wissenschaftliche Diskussion über Multi-Agenten-Systeme (MAS) konzentrieren sich für gewöhnlich auf die Agenten selbst, die Agentenmodelle, sowie die Elemente und Effekte, die sie in ihrer Umgebung wahrnehmen. Diese Betrachtung lässt jedoch mögliche Probleme in einer späteren Implementierung aus, die von Einschränkungen in Datenmodellen, Schnittstellen, oder Protokollen herrühren können, über die Agenten und ihre Umgebung Informationen miteinander austauschen. Heutzutage ist sich die Forschungsgemeinschaft einig, dass die Umgebung als solche als Abstraktionsschicht verstanden werden sollte, über die Agenten Umgebungseffekte und -elemente wahrnehmen, interpretieren, und mit ihnen interagieren. Diese Betrachtungsweise verschleiert jedoch die Trennung zwischen der Umgebung als die Sammlung interaktiver Elemente und wahrnehmbarer Phänomene auf der einen Seite, und der zugrundeliegenden Technologie, über die diese Information den Agenten bereitgestellt wird, auf der anderen. Diese Dissertation schlägt als Lösung vor, zusätzlich zu Agenten undUmgebung ein digitales Medium, über das Agenten die Umgebung bereitgestellt wird, als drittes Element von Multi-Agenten-Systemen zu betrachten. Der Begriff "Medium" bezieht sich dann genau auf diese technologische Komponente, über die Umgebungsinformationen Agenten interaktiv bereitgestellt werden, und über die Agenten die zugrundeliegenden Daten wahrnehmen, interpretieren, und letztendlich modifizieren. Desweiteren wird diese Dissertation aufzeigen, wie die Eigenschaften eines sorgfältig gewählten Mediums ausgenutzt werden können, um ein koordiniertes Systemverhalten zu erreichen. Ein geeigneter Kandidat für ein digitales Agentenmedium findet sich im Ökosystem des „Semantic Web”, in Form von „Linked Data”, wörtlich („verknüpfte Daten”). Zusätzlich zu einer konzeptionellen Diskussion über die Natur digitaler Agenten- Media, spezifiziert diese Dissertation „Linked Data” als Agentenmedium detailliert aus, und beschreibt im Detail die Mittel, wie sich MAS um Linked Data Technologien herum implementieren lassen

A Survey on the Web of Things

The Web of Things (WoT) paradigm was proposed first in the late 2000s, with the idea of leveraging Web standards to interconnect all types of embedded devices. More than ten years later, the fragmentation of the IoT landscape has dramatically increased as a consequence of the exponential growth of connected devices, making interoperability one of the key issues for most IoT deployments. Contextually, many studies have demonstrated the applicability of Web technologies on IoT scenarios, while the joint efforts from the academia and the industry have led to the proposals of standard specifications for developing WoT systems. Through a systematic review of the literature, we provide a detailed illustration of the WoT paradigm for both researchers and newcomers, by reconstructing the temporal evolution of key concepts and the historical trends, providing an in-depth taxonomy of software architectures and enabling technologies of WoT deployments and, finally, discussing the maturity of WoT vertical markets. Moreover, we identify some future research directions that may open the way to further innovation on WoT systems

SL-RI: Integration of supervised learning in robots for industry 5.0 automated application monitoring

Robotic technology holds a significant role within the realm of smart industries, wherein all functionalities are executed within real-time systems. The verification of robot operations is a crucial aspect in the context of Industry 5.0. To address this requirement, a distinctive design methodology known as SL-RI is proposed. This article aims to establish the significance of incorporating robots in the Industry 5.0 framework through analytical representations. In the context of this industrial monitoring system, the implementation of a supplementary algorithm is essential for effective management, as it enables the robots to acquire knowledge through the analysis and adaptation of restructured commands. The analytical model of robots is designed to accurately monitor the precise position and accelerations of robots, resulting in full-scale representations with minimal error conditions. The uniqueness of the proposed method in robotic monitoring system is related to the application process that is directly applied in Industry 5.0 by using various parametric cases where active movement of robots are monitored with rotational matrix representations. In this type of representations the significance relies in the way to understand the full movement of robots across various machines and its data handling characteristics that provides low loss and error factors

Enabling Technologies for Web 3.0: A Comprehensive Survey

Web 3.0 represents the next stage of Internet evolution, aiming to empower users with increased autonomy, efficiency, quality, security, and privacy. This evolution can potentially democratize content access by utilizing the latest developments in enabling technologies. In this paper, we conduct an in-depth survey of enabling technologies in the context of Web 3.0, such as blockchain, semantic web, 3D interactive web, Metaverse, Virtual reality/Augmented reality, Internet of Things technology, and their roles in shaping Web 3.0. We commence by providing a comprehensive background of Web 3.0, including its concept, basic architecture, potential applications, and industry adoption. Subsequently, we examine recent breakthroughs in IoT, 5G, and blockchain technologies that are pivotal to Web 3.0 development. Following that, other enabling technologies, including AI, semantic web, and 3D interactive web, are discussed. Utilizing these technologies can effectively address the critical challenges in realizing Web 3.0, such as ensuring decentralized identity, platform interoperability, data transparency, reducing latency, and enhancing the system's scalability. Finally, we highlight significant challenges associated with Web 3.0 implementation, emphasizing potential solutions and providing insights into future research directions in this field

Mapping Industry 4.0 Enabling Technologies into United Nations Sustainability Development Goals

The emerging of the fourth industrial revolution, also known as Industry 4.0 (I4.0), from the advancement in several technologies is viewed not only to promote economic growth, but also to enable a greener future. The 2030 Agenda of the United Nations for sustainable development sets out clear goals for the industry to foster the economy, while preserving social well-being and ecological validity. However, the influence of I4.0 technologies on the achievement of the Sustainable Development Goals (SDG) has not been conclusively or systematically investigated. By understanding the link between the I4.0 technologies and the SDGs, researchers can better support policymakers to consider the technological advancement in updating and harmonizing policies and strategies in different sectors (i.e., education, industry, and governmental) with the SDGs. To address this gap, academic experts in this paper have investigated the influence of I4.0 technologies on the sustainability targets identified by the UN. Key I4.0 element technologies have been classified to enable a quantitative mapping with the 17 SDGs. The results indicate that the majority of the I4.0 technologies can contribute positively to achieving the UN agenda. It was also found that the effects of the technologies on individual goals varies between direct and strong, and indirect and weak influences. The main insights and lessons learned from the mapping are provided to support future policy

Value creation with digital twins : application-oriented conceptual framework and case study

The internet of things, digital twins of smart connected products, and thereby enabled smart services are topics of great interest and have been gaining traction for many years. However, many questions concerning the application-oriented usage of digital twins still need to be scrutinized. Therefore, this paper examines the question of an application-oriented framework for value creation with digital twins using design science research approaches. A conceptual reference framework is presented based on earlier research and iteratively developed within workshops with three companies. The framework incorporates primary dimensions of external and internal value creation and data resources. Further, it discusses the product life cycle, the real-world counterpart, value creation in the ecosystem, and the generational aspect of the digital twins. Furthermore, applying the framework to a use case with an industrial research partner helps to show the contributions to the industrial sector. The framework provides utility to practitioners as a means of creating a common sense in interdisciplinary teams, communicating digital twin projects to internal and external stakeholders, and as a toolbox for specific challenges concerning digital twins. In addition, the framework distinguishes itself from existing approaches by including the service ecosystem and its actors while considering the principles of product life cycle management. Therefore, using the framework in other use cases will test the approach on different industries and products. Furthermore, there is a need to develop approaches for implementing and developing an existing case

Graph Neural Networks for Anomaly Detection in Industrial Internet of Things

This is the author accepted manuscript. The final version is available from Institute of Electrical and Electronics Engineers via the DOI in this record.The Industrial Internet of Things (IIoT) plays an important role in digital transformation of traditional industries towards Industry 4.0. By connecting sensors, instruments and other industry devices to the Internet, IIoT facilitates the data collection, data analysis, and automated control, thereby improving the productivity and efficiency of the business as well as the resulting economic benefits. Due to the complex IIoT infrastructure, anomaly detection becomes an important tool to ensure the success of IIoT. Due to the nature of IIoT, graph-level anomaly detection has been a promising means to detect and predict anomalies in many different domains such as transportation, energy and factory, as well as for dynamically evolving networks. This paper provides a useful investigation on graph neural networks (GNN) for anomaly detection in IIoT-enabled smart transportation, smart energy and smart factory. In addition to the GNN-empowered anomaly detection solutions on point, contextual, and collective types of anomalies, useful datasets, challenges and open issues for each type of anomalies in the three identified industry sectors (i.e., smart transportation, smart energy and smart factory) are also provided and discussed, which will be useful for future research in this area. To demonstrate the use of GNN in concrete scenarios, we show three case studies in smart transportation, smart energy, and smart factory, respectively.Engineering and Physical Sciences Research Council (EPSRC)National Natural Science Foundation of China (NSFC)Macao Science and Technology Development FundOpen Fund of Zhejiang La