How Industry 4.0 Can Benefit From Semantic Web Technologies and Artefacts

2021 ◽  
Vol 13 (4) ◽  
pp. 64-74
Author(s):  
Leila Zemmouchi-Ghomari
Keyword(s):  
Cloud Computing ◽  
Internet Of Things ◽  
Semantic Web ◽  
Industry 4.0 ◽  
The Internet ◽  
Time Data ◽  
Semantic Web Technologies ◽  
Web Technologies ◽  
Real Time Data ◽  
The Internet Of Things

Industry 4.0 is a technology-driven manufacturing process that heavily relies on technologies, such as the internet of things (IoT), cloud computing, web services, and big real-time data. Industry 4.0 has significant potential if the challenges currently being faced by introducing these technologies are effectively addressed. Some of these challenges consist of deficiencies in terms of interoperability and standardization. Semantic Web technologies can provide useful solutions for several problems in this new industrial era, such as systems integration and consistency checks of data processing and equipment assemblies and connections. This paper discusses what contribution the Semantic Web can make to Industry 4.0.

scholarly journals Enabling Interoperability in the Internet of Things

2017 ◽  
Vol 13 (1) ◽  
pp. 147-167 ◽  
Author(s):  
Alfredo D'Elia ◽  
Fabio Viola ◽  
Luca Roffia ◽  
Paolo Azzoni ◽  
Tullio Salmon Cinotti
Keyword(s):  
Internet Of Things ◽  
Semantic Web ◽  
Smart Cities ◽  
The Internet ◽  
Semantic Web Technologies ◽  
Public Authorities ◽  
Web Technologies ◽  
Data Store ◽  
Rdf Graphs ◽  
The Internet Of Things

Semantic Web technologies act as an interoperability glue among different formats, protocols and platforms, providing a uniform vision of heterogeneous devices and services in the Internet of Things (IoT). Semantic Web technologies can be applied to a broad range of application contexts (i.e., industrial automation, automotive, health care, defense, finance, smart cities) involving heterogeneous actors (i.e., end users, communities, public authorities, enterprises). Smart-M3 is a semantic publish-subscribe software architecture conceived to merge the Semantic Web and the IoT domains. It is based on a core component (SIB, Semantic Information Broker) where data is stored as RDF graphs, and software agents using SPARQL to update, retrieve and subscribe to changes in the data store. This article describes a OSGi SIB implementation extended with a new persistent SPARQL update primitive. The OSGi SIB performance has been evaluated and compared with the reference C implementation. Eventually, a first porting on Android is presented.

scholarly journals Smart Hybrid Manufacturing Control Using Cloud Computing and the Internet-of-Things

Machines ◽  
2018 ◽  
Vol 6 (4) ◽  
pp. 62 ◽  
Author(s):  
Jonnro Erasmus ◽  
Paul Grefen ◽  
Irene Vanderfeesten ◽  
Konstantinos Traganos
Keyword(s):  
Cloud Computing ◽  
Information System ◽  
Internet Of Things ◽  
Industry 4.0 ◽  
Smart Devices ◽  
The Internet ◽  
Hybrid Manufacturing ◽  
Architecture Design ◽  
Reference Architecture ◽  
The Internet Of Things

Industry 4.0 is expected to deliver significant gains in productivity by assimilating several technological advancements including cloud computing, the Internet-of-Things, and smart devices. However, it is unclear how these technologies should be leveraged together to deliver the promised benefits. We present the architecture design of an information system that integrates these technologies to support hybrid manufacturing processes, i.e., processes in which human and robotic workers collaborate. We show how well-structured architecture design is the basis for a modular, complex cyber-physical system that provides horizontal, cross-functional manufacturing process management and vertical control of heterogenous work cells. The modular nature allows the extensible cloud support enhancing its accessibility to small and medium enterprises. The information system is designed as part of the HORSE Project: a five-year research and innovation project aimed at making recent technological advancements more accessible to small and medium manufacturing enterprises. The project consortium includes 10 factories to represent the typical problems encountered on the factory floor and provide real-world environments to test and evaluate the developed information system. The resulting information system architecture model is proposed as a reference architecture for a manufacturing operations management system for Industry 4.0. As a reference architecture, it serves two purposes: (1) it frames the scientific inquiry and advancement of information systems for Industry 4.0 and (2) it can be used as a template to develop commercial-grade manufacturing applications for Industry 4.0.

scholarly journals Ontology-Defined Middleware for Internet of Things Architectures

Sensors ◽  
2019 ◽  
Vol 19 (5) ◽  
pp. 1163 ◽  
Author(s):  
Víctor Caballero ◽  
Sergi Valbuena ◽  
David Vernet ◽  
Agustín Zaballos
Keyword(s):  
Internet Of Things ◽  
Data Exchange ◽  
The Internet ◽  
Semantic Web Technologies ◽  
Web Of Things ◽  
Web Technologies ◽  
Massive Number ◽  
The Internet Of Things ◽  
Exchange Data ◽  
The Web

The Internet of Things scenario is composed of an amalgamation of physical devices. Those physical devices are heterogeneous in their nature both in terms of communication protocols and in data exchange formats. The Web of Things emerged as a homogenization layer that uses well-established web technologies and semantic web technologies to exchange data. Therefore, the Web of Things enables such physical devices to the web, they become Web Things. Given such a massive number of services and processes that the Internet of Things/Web of Things enables, it has become almost mandatory to describe their properties and characteristics. Several web ontologies and description frameworks are devoted to that purpose. Ontologies such as SOSA/SSN or OWL-S describe the Web Things and their procedures to sense or actuate. For example, OWL-S complements SOSA/SSN in describing the procedures used for sensing/actuating. It is, however, not its scope to be specific enough to enable a computer program to interpret and execute the defined flow of control. In this work, it is our goal to investigate how we can model those procedures using web ontologies in a manner that allows us to directly deploy the procedure implementation. A prototype implementation of the results of our research is implemented along with an analysis of several use cases to show the generality of our proposal.

Real-time data analytics in healthcare using the Internet of Things

2020 ◽  
pp. 37-50
Author(s):  
Satya Narayan Sahu ◽  
Maheswata Moharana ◽  
Purna Chandra Prusti ◽  
Shanta Chakrabarty ◽  
Fahmida Khan ◽  
...  
Keyword(s):  
Internet Of Things ◽  
Real Time ◽  
Data Analytics ◽  
The Internet ◽  
Time Data ◽  
Real Time Data ◽  
The Internet Of Things

scholarly journals Modern technologies in Industry 4.0 – cyber-physical systems

2020 ◽  
Vol 7 (3) ◽  
Author(s):  
Anna Smyshlyaeva ◽  
Kseniya Reznikova ◽  
Denis Savchenko
Keyword(s):  
Cloud Computing ◽  
Big Data ◽  
Internet Of Things ◽  
Industry 4.0 ◽  
Cyber Physical Systems ◽  
The Internet ◽  
Physical Systems ◽  
Level Data ◽  
The Internet Of Things ◽  
Modern Technologies

With the advent of the Industry 4.0 concept, the approach to production automation has fundamentally changed. The manufacturing industry is based on such modern technologies as the Internet of Things, Big Data, cloud computing, artificial intelligence and cyber-physical systems. These technologies have proven themselves not only in industry, but also in various other branches of life. In this paper, the authors consider the concept of cyber-physical systems – systems based on the interaction of physical processes with computational ones. The article presents a conceptual model of cyber-physical systems that displays its elements and their interaction. In cyber-physical systems, it represents five levels: physical, network, data storage, processing and analytics level, application level. Cyber-physical systems carry out their work using a basic set of technologies: the Internet of things, big data and cloud computing. Additional technologies are used depending on the purpose of the system. At the physical level, data is collected from physical devices. With the help of the Internet of Things at the network level, data is transferred to a data warehouse for further processing or processed almost immediately thanks to cloud computing. The amount of data in cyber-physical systems is enormous, so it is necessary to use big data technology and effective methods for processing and analyzing this data. The main feature of this technological complex is real-time operation. Despite the improvement in the quality of production and human life, cyber-physical systems have a number of disadvantages. The authors highlight the main problems of cyber-physical systems and promising areas of research for their development. Having solved the listed problems, cyber-physical systems will reach a qualitatively new level of utility. The paper also provides examples of the implementation of concepts such as a smart city, smart grid, smart manufacturing, smart house. These concepts are based on the principle of cyber-physical systems.

scholarly journals Impacts of Internet of Things on Supply Chains: A Framework for Warehousing

2019 ◽  
Vol 8 (3) ◽  
pp. 84 ◽  
Author(s):  
Noha Mostafa ◽  
Walaa Hamdy ◽  
Hisham Alawady
Keyword(s):  
Supply Chain ◽  
Internet Of Things ◽  
Industry 4.0 ◽  
Inventory Level ◽  
The Internet ◽  
Time Data ◽  
Basic Part ◽  
Information And Communication ◽  
Potential Benefits ◽  
The Internet Of Things

The emergence of new digital industrial technology, known as Industry 4.0, has a positiveimpact on the performance of the supply chain. Warehouses are a basic part of the supply chain;they are used to store products and manage the inventory level. A sound warehouse managementsystem can lead to cost reduction and also can improve customer satisfaction. Traditionalwarehouse management models have become less efficient and unsuitable for today’s increasingmarket requirements. For the past decades, information and communication technology has beenused for warehouse management. This paper presents a new approach for warehouse managementby utilizing one of the main pillars of Industry 4.0, the Internet of Things. This new technologyenables the connection of several objects through collecting real-time data and sharing them; theresulting information can then be used to support automated decision-making. The architecture ofthis application is illustrated and its potential benefits are overviewed. A framework is proposed toimplement this approach in warehousing management, which can help in providing real-timevisibility of everything in the warehouse, increasing speed and efficiency, and preventing inventoryshortage and counterfeiting. This proposal gives an effective roadmap for enterprises to improvetheir warehouses by using the Internet of Things.

scholarly journals TEACHING INDUSTRY 4.0

2020 ◽  
Vol 2 ◽  
pp. 69
Author(s):  
Gilberto Marzano ◽  
Andris Martinovs
Keyword(s):  
Machine Learning ◽  
Internet Of Things ◽  
Industry 4.0 ◽  
Industrial Revolution ◽  
Data Access ◽  
The Internet ◽  
German Industry ◽  
Time Data ◽  
Future Challenges ◽  
The Internet Of Things

Industry 4.0 is a term that was introduced by the German government at the time of the Hannover Fair in 2011 in relation to an initiative brought forward to support German industry in addressing future challenges. It refers to the 4th industrial revolution, in which disruptive digital technologies, such as the Internet of Things (IoT), robotics, virtual reality (VR), and artificial intelligence (AI), are exercising a notable impact on industrial production.Industry 4.0 takes the emphasis on digital technology of recent decades to a whole new level with the help of interconnectivity through the Internet of Things (IoT), real-time data access, and the introduction of cyber-physical systems.This paper focuses on the design of an educational module for higher education mechatronics students. Introducing Industry 4.0 into a mechatronics curriculum will reinforce the integration of student competences in flexible and rapid manufacturing. The module includes notions of machine learning and deep machine learning, which are essential in robotics and behavioral robotics and closely interact with control theory. The results of a pilot training activity in the field are also illustrated and discussed. 

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