FEDTWIN – trustworthy digital twin as a service for visually impaired

– The thought of digital twins has gained substantial attention in recent years due to its potential to transform various industries, including renewable energy. Digital twins involve the creation of virtual models that mirror the behaviour and characteristics of real-world physical systems. In the perspective of solar plants, digital twins have emerged as a promising tool to enhance performance monitoring, predictive maintenance, and overall operational efficiency. Digital twin engineering, characterized by its dynamic data modelling of industrial assets, offers a disruptive technology capable of adapting to real-time changes in the environment and operations. This living model can predict future infrastructure behaviour and proactively identify potential issues within the physical system. The article highlights the essential components of the digital twin ecosystem, such as sensor technologies, the Industrial Internet of Things, simulation, modelling, and machine learning, underscoring their relevance in predictive maintenance applications. This review provides an in-extensive review of the development and application of digital twins for predicting and mitigating faults and defects in solar power plants. It opens with a look at current developments, underlining the rising focus on digital twins for optimizing solar farms. It begins with an overview of existing solutions in the field, highlighting the growing interest in leveraging digital twin technology to enhance solar plant operations. Additionally, the article outlines the implementation stage of a prototype digital twin for a solar power plant.

This study uses a wind turbine case study as a subdomain of Industrial Internet of Things (IIoT) to showcase an architecture for implementing a distributed digital twin in which all important aspects of a predictive maintenance solution in a DT use a fog computing paradigm, and the typical predictive maintenance DT is improved to offer better asset utilization and management through real-time condition monitoring, predictive analytics, and health management of selected components of wind turbines in a wind farm. Digital twin (DT) is a technology that sits at the intersection of Internet of Things, Cloud Computing, and Software Engineering to provide a suitable tool for replicating physical objects in the digital space. This can facilitate the implementation of asset management in manufacturing systems through predictive maintenance solutions leveraged by machine learning (ML). With DTs, a solution architecture can easily use data and software to implement asset management solutions such as condition monitoring and predictive maintenance using acquired sensor data from physical objects and computing capabilities in the digital space. While DT offers a good solution, it is an emerging technology that could be improved with better standards, architectural framework, and implementation methodologies. Researchers in both academia and industry have showcased DT implementations with different levels of success. However, DTs remain limited in standards and architectures that offer efficient predictive maintenance solutions with real-time sensor data and intelligent DT capabilities. An appropriate feedback mechanism is also needed to improve asset management operations.

Biopharma Is Going Digital … Bit by Bit

This review article provides an overview of the potential of the Industrial Internet of Things (IIoT) to revolutionize industrial automation. The IIoT is the Internet of Things (IoT) but in an industrial context, i.e., IIoT is used more to connect machines and devices in industrial environments. The IIoT has the potential to benefit from advances in artificial intelligence, particularly machine learning and deep learning, to increase efficiency and productivity and reduce overhead costs. We provide an overview of the supervisory control and data acquisition system, a definition of IIoT, and how IIoT can offer industry greater potential for system integration to improve automation and optimization. In addition, five of the major IIoT protocols are discussed, namely, message queue telemetry transport, advanced messaging queuing protocol, constrained application protocol, data distribution service, and open platform communication unified architecture. We then identified key IIoT improvements for industrial automation. These are; efficient and low-cost systems, digital twin, machine failure prediction, real-time remote monitoring, and security. We then discussed the key research in the literature for each category. We presented some public IIoT datasets so that researchers can use them to develop new learning models to improve the security of IIoT systems. Finally, we discussed some of the limitations, recommendations, and future perspectives for developing IIoT-enabled systems.

A digital twin is a powerful new concept in computational modelling that aims to produce a one-to-one mapping of a physical structure, operating in a specific context, into the digital domain. The development of a digital twin provides clear benefits in improved predictive performance and in aiding robust decision making for operators and asset managers. One key feature of a digital twin is the ability to improve the predictive performance over time, via improvements of the digital twin. An important secondary function is the ability to inform the user when predictive performance will be poor. If regions of poor performance are identified, the digital twin must offer a course of action for improving its predictive capabilities. In this paper three sources of improvement are investigated; (i) better estimates of the model parameters, (ii) adding/updating a data-based component to model unknown physics, and (iii) the addition of more physics-based modelling into the digital twin. These three courses of actions (along with taking no further action) are investigated through a probabilistic modelling approach, where the confidence of the current digital twin is used to inform when an action is required. In addition to addressing how a digital twin targets improvement in predictive performance, this paper also considers the implications of utilising a digital twin in a control context, particularly when the digital twin identifies poor performance of the underlying modelling assumptions. The framework is applied to a three-storey shear structure, where the objective is to construct a digital twin that predicts the acceleration response at each of the three floors given an unknown (and hence, unmodelled) structural state, caused by a contact nonlinearity between the upper two floors. This is intended to represent a realistic challenge for a digital twin, the case where the physical twin will degrade with age and the digital twin will have to make predictions in the presence of unforeseen physics at the time of the original model development phase.

Digital Twins are starting to revolutionize many industries in the last decade providing a plethora of benefits to optimize the performance of industrial systems. They aim to create a continuously synchronized model of the physical system which enables rapid adaptation to dynamics, mainly unpredicted and undesirable changes. A wide range of industrial fields have already benefited from digital twins technology such as aerospace, manufacturing, healthcare, city management and maritime and shipping. Furthermore, recent research works are starting to study the integration of digital twins for computer networks to allow more innovation and intelligent management. One of the basic building blocks of digital twins technology is the Internet of Things where wireless sensors and actuators are deployed to ensure the interaction between the physical and digital worlds. This type of network is complex to manage due to its severe constraints especially when it controls critical industrial applications, resulting in the Industrial Internet of Things (IIoT). We believe that the optimization of the IIoT will lead to efficient integration of Digital Twins in Industry 4.0. In this paper, we design a Network Digital Twin for the IIoT where sensors, actuators and communication infrastructure are replicated in the digital twin to enable intelligent real-time management of such networks. This way, new networking services such as predictive maintenance, network diagnosis, resource allocation, energy optimization with other intelligent services can be efficiently integrated and exploited in the network life-cycle. We validate the proposed architecture by providing a promising prototype implementation that should unleash the full potential of the network digital twin.

ABSTRACTThe integration of the Internet of Things (IoT) into consumer devices has driven the evolution of the Industrial Internet of Things (IIoT), also known as Industry 4.0 (I4.0), extending connectivity to industrial settings where benefits such as increased efficiency, automation, and predictive maintenance are transforming processes. However, with these advancements comes a host of cybersecurity challenges unique to IIoT, including the longevity of industrial components and the expansive scale of interconnected networks, which differ from security needs in Consumer IoT (C‐IoT). In parallel, the healthcare sector has seen similar technological integration through the Healthcare Internet of Things (H‐IoT) and the progression to Healthcare 4.0 (HC4.0), emphasizing data‐driven patient care and seamless digital health services. This paper presents a comprehensive and systematic review of emerging cybersecurity technologies in healthcare, focusing on IoT, IIoT, H‐IoT, and HC4.0 applications. Our study examines recent advancements in cybersecurity protocols and identifies critical security challenges that arise from the increased reliance on these technologies. Specifically, we aim to highlight how these interconnected frameworks can enhance patient data protection, ensure resilience against cyber threats, and strengthen healthcare systems' operational integrity. Key areas of focus include data privacy, network vulnerabilities, and the risks of cyber‐attacks in healthcare contexts, with an emphasis on the necessity of robust and adaptive security measures to safeguard sensitive healthcare information. Furthermore, this survey synthesizes current research on security frameworks and protocols tailored to HC4.0 applications, offering an in‐depth analysis of their strengths, limitations, and applicability in real‐world scenarios. We identify gaps in the literature and propose future research directions aimed at advancing encryption, authentication, and network resilience in interconnected healthcare systems. The concluding sections address ongoing challenges, open issues, and the need for scalable and interoperable security solutions to support the seamless integration of IoT technologies across healthcare and industrial sectors. By providing a holistic overview of the cybersecurity landscape in IoT, IIoT, and H‐IoT, this paper contributes valuable insights to the development of secure, resilient, and sustainable systems in an increasingly connected world.

9 - Digital Twin Development and cloud deployment for a DC Motor Control embedded system

In today’s world we are surrounded by various types of technologies on which the dependency of humans has increased because these technologies help humans to connect and reduce their workload. In this paper, we have discussed two technologies that are cloud services and Internet of things (IoT). We have combined these two technologies as one technology. Google Cloud Platform and Microsoft Azure are the two cloud service providers along with the role of the Internet of things in Healthcare. The role of the Internet of things in healthcare rapidly increased the use of application services in a cloud-based IT infrastructure. So, according to the definition of the Internet of things, devices are connected to one another through network and data exchange with the help of this network. An Introduction to the Google Cloud Platform (GCP) and Microsoft Azure, moving to the comparison between the two along with the growth of Google Cloud Service and Microsoft Azure with respect to time. This paper attempts to show last 5 years’ growth of these two cloud-based technologies. Then we have described the architecture of the Internet of things (IoT) in the Healthcare domain. Internet of things (IoT) uses cloud-based technology, so the paper discusses Google Cloud and Microsoft Azure cloud and explores Google healthcare service. In Google Healthcare, factors considered in pricing scheme along with request plan offered by Google cloud Healthcare service, applications of the internet of things (IoT), its merits and demerits. The future of this technology in the healthcare sector is also studied.KeywordsGoogle Cloud Platform (GCP)Microsoft AzureHealthcareInternet of Things (IoT)eHealthVirtual machines (VMs)

Digital Twin Network for the IIoT using Eclipse Ditto and Hono

Developing Digital Twins for energy efficiency in the production phase of products

Securing IIoT applications in 6G and beyond using adaptive ensemble learning and zero-touch multi-resource provisioning

The article is devoted to a comparative analysis of cloud services for processing geographic data. It describes in detail the services - Google Cloud, Amazon Web Services and Microsoft Azure - that provide tools for storing, processing and analyzing large amounts of geographic data. The article also describes the parameters of geoinformation services, the access algorithm, and examples of program code for processing satellite data. The article describes such opportunities and limitations of using cloud services as automation, security and scalability. The conclusions and recommendations for further development of geographic information systems based on cloud services are provided. Services. Amazon Web Services (AWS), Microsoft Azure, and Google Cloud Platform (GCP) offer a variety of geodata storage solutions. These solutions include object storage, such as Amazon S3, Azure Blob Storage, and Google Cloud Storage, as well as geospatial databases, such as Amazon RDS, Azure Cosmos DB, and Google Cloud Firestore. In addition, each of these services provides a set of services for analyzing and processing geographic information data. For example, AWS offers services such as Amazon Athena, Amazon Redshift, and AWS Glue, which allow you to run SQL queries, conduct analytics, and integrate geodata with other services. Azure offers services such as Azure SQL Database, Azure Databricks, and HDInsight, which provide capabilities for processing and analyzing geographic data. GCP also provides services such as BigQuery, Dataflow, and Dataproc, which allow you to perform analytical operations and process large amounts of geodata. Support for integration with various geo-tools is important for analysis, such as AWS, Amazon Location Service, Amazon Ground Truth, and Amazon Rekognition, which allow you to work with geodata at different levels of complexity. Azure has Azure Maps, which provides services for geocoding, routing, and visualization of geodata. GCP also offers Google Maps Platform, which provides extensive integration with geographic technologies such as routing, geocoding, and map visualization. All these processes will allow for more efficient data processing. Keywords: cloud technologies,

ABSTRACTOrganizations continuously develop Digital Twins across a wide number of applications and industries. While this represents a testimony to the benefits and opportunities Digital Twins provide (AIAA 2022), their development, maintenance and evolution still face major challenges (Bordeleau et al. 2020). Most past and current efforts focusing on the development of Digital Twins have relied on ad‐hoc approaches, where most of the efforts start with building models without properly framing the problem (Martin 2019, Lu et al. 2020). More importantly it has led to the development of models that provide a solution to the wrong problem and consequently fail to address the core questions and needs of the stakeholders (Martin 2019)]. The development and implementation of Digital Twins also lack standardization (Shao 2021), instead relying on bespoke methods and technologies (Niederer et al. 2021), which in turns leads to a lack of consistency in their description and implementation, as well as limited interoperability across applications, tools and disciplines (Niederer et al. 2021, Piroumian 2021). The development of Digital Twins has been plagued by a lack of scalable approaches, leading to implementations that are highly specialized and require considerable resources in terms of subject matter expertise (Niederer et al. 2021).The development of standardized methodologies has been identified as a means to unleash the full potential of Digital Twins (Shao 2021, Piroumian 2021) and increase their adoption across a wider range of disciplines and applications (Niederer et al. 2021). To that end, this paper presents a domain agnostic and descriptive reference model, which builds on recognized industry practices and guidelines, to support the planning, description, and analysis of Digital Twins. It also introduces real‐world examples of aerospace Digital Twin use cases and discusses how the generic reference model supports the various use case applications. Finally, this paper briefly discusses recommendations and next steps on how to realize value from Digital Twins more broadly.

This paper aims to address key considerations in building digital twins for some of the most commonly used electrical machines, including the Induction Machine with a Squirrel Cage Rotor, DC Machine with Linear Electrical Excitation, DC Machine with Permanent Excitation, Synchronous Machine with Electrical Excitation and Damper, and Synchronous Machine with Permanent Excitation and Damper. These machines, critical across various industries, require precise modeling and real-time monitoring to optimize their performance and lifespan. The development of digital twins for these machines involves creating virtual representations that dynamically mirror their physical counterparts, using real-time data from sensors and advanced simulation techniques. Each machine type presents unique challenges for the digital twin, such as accurately capturing the electromagnetic interactions in the squirrel cage rotor or modeling the excitation systems in synchronous and DC machines. This paper investigates the methodologies for overcoming these challenges, focusing on data acquisition, mathematical modeling, real-time analytics, and predictive diagnostics. The paper also highlights the benefits of digital twin technology, including enhanced operational efficiency, reduced downtime through predictive maintenance, and optimized control under varying load conditions. Additionally, it discusses the integration of digital twins into broader Industrial Internet of Things (IIoT) frameworks, paving the way for more connected and autonomous industrial environments. The findings in this paper provide valuable insights for both researchers and engineers seeking to implement digital twins in electrical machine systems, contributing to improved industrial automation and machine lifecycle management