With the continuous evolution of digital twins, the requirements of interconnection and interoperability have led to the creation of the term Distributed Digital Twin, where commonly, different components of the same digital twin operate on different devices. This article addresses this new gap in the field by combining Digital Twins and Distributed systems technologies and introduces a re-definition of the architecture of OpenTwins, an open-source platform designed to develop generic next-gen 3D-IoT-AI-powered digital twins. This approach enables the distribution of digital twins across different infrastructures by seamlessly integrating multiple instances of OpenTwins working together. Distributing Digital Twins across networks and devices can offer dynamic and collaborative simulations, artificial intelligence techniques, yet poses synchronization and scalability challenges. The platform re-definition involves the definition of a lightweight, synchronized, and distributed version of the original OpenTwins architecture to tackle these issues. As the field of digital twins is closely linked to the Internet of Things environment and Industry 4.0, this architecture has been designed to be compatible in IoT devices, being compatible with ARM architectures, consuming fewer resources than the original platform as it has fewer components, thus reducing the number of messages and the bandwidth consumed.

Abstract The use of serverless computing has been gaining popularity in recent years as an alternative to traditional Cloud computing. We explore the usability and potential development benefits of three popular open‐source serverless platforms in the context of IoT: OpenFaaS, Fission, and OpenWhisk. To address this we discuss our experience developing a serverless and low‐latency Distributed Deep Neural Network (DDNN) application. Our findings indicate that these serverless platforms require significant resources to operate and are not ideal for constrained devices. In addition, we archived a 55% improvement compared to Kafka‐ML's performance under load, a framework without dynamic scaling support, demonstrating the potential of serverless computing for low‐latency applications.

Abstract The realm of digital twins is experiencing rapid growth and presents a wealth of opportunities for Industry 4.0. In conjunction with traditional simulation methods, digital twins offer a diverse range of possibilities. However, many existing tools in the domain of open‐source digital twins concentrate on specific use cases and do not provide a versatile framework. In contrast, the open‐source digital twin framework, OpenTwins, aims to provide a versatile framework that can be applied to a wide range of digital twin applications. In this article, we introduce a re‐definition of the original OpenTwins platform that enables the management of custom simulation services and the management of FMI simulation services, which is one of the most widely used simulation standards in the industry and its coexistence with machine learning models, which enables the definition of the next‐gen digital twins. Thanks to this integration, digital twins that reflect reality better can be developed, through hybrid models, where simulation data can feed the scarcity of machine learning data and so forth. As part of this project, a simulation model developed through the hydraulic software Epanet was validated in OpenTwins, in addition to an FMI simulation service. The hydraulic model was implemented and tested in an agricultural use case in collaboration with the University of Córdoba, Spain. A machine learning model has been developed to assess the behavior of an FMI simulation through machine learning.

Machine Learning (ML) and Artificial Intelligence (AI) depend on data sources to train, improve, and make predictions through their algorithms. With the digital revolution and current paradigms like the Internet of Things, this information is turning from static data to continuous data streams. However, most of the ML/AI frameworks used nowadays are not fully prepared for this revolution. In this paper, we propose Kafka-ML, a novel and open-source framework that enables the management of ML/AI pipelines through data streams. Kafka-ML provides an accessible and user-friendly Web user interface where users can easily define ML models, to then train, evaluate, and deploy them for inferences. Kafka-ML itself and the components it deploys are fully managed through containerization technologies, which ensure their portability, easy distribution, and other features such as fault-tolerance and high availability. Finally, a novel approach has been introduced to manage and reuse data streams, which may eliminate the need for data storage or file systems.

The current dependency of Artificial Intelligence (AI) systems on Cloud computing implies higher transmission latency and bandwidth consumption. Moreover, it challenges the real-time monitoring of physical objects, e.g., the Internet of Things (IoT). Edge systems bring computing closer to end devices and support time-sensitive applications. However, Edge systems struggle with state-of-the-art Deep Neural Networks (DNN) due to computational resource limitations. This paper proposes a technology framework that combines the Edge-Cloud architecture concept with BranchyNet advantages to support fault-tolerant and low-latency AI predictions. The implementation and evaluation of this framework allow assessing the benefits of running Distributed DNN (DDNN) in the Cloud-to-Things continuum. Compared to a Cloud-only deployment, the results obtained show an improvement of 45.34% in the response time. Furthermore, this proposal presents an extension for Kafka-ML that reduces rigidness over the Cloud-to-Things continuum managing and deploying DDNN.

In the last few years, the Internet of Things (IoT) has emerged as the new disruptive technology to change the world. Cloud computing has accompanied this field to overcome its processing and storage limitations. However, this evolution has originated a huge increase in IoT devices and data that will create a bottleneck for current networks, in addition to a lack of low latency in cloud communications. Edge computing has been developed to address this challenge, moving the processing to the edge of the network. In this paper, an edge computing architecture is presented to overcome these challenges. The architecture, based on our previous work on the λ-CoAP architecture, covers the whole vision of an edge computing deployment, from IoT devices, to the edge Smart Gateways and up to a cloud infrastructure.

In the Internet of Things (IoT) a worldwide network of sensors and actuators transmit data and actuate over the Internet. Nevertheless, the deployment of sensors and actuators usually requires tools to program and configure them before they can work, and not everyone has such access. Current smartphones are provided with a large set of sensors and actuators that can be incorporated in the IoT. With the aim of achieving seamless integration, this paper presents SocICoAP, a system that enables the sharing of sensors and actuators present in smartphones and installing custom ones at run-time in microcontrollers. In this way smartphones are integrated into the IoT, not only a sensor providing data from its built-in sensors but also as a gateway for nearby deployed sensors. Furthermore, the sensor data can also be globally shared and analysed through a cloud computing integration.

SUMMARY The development of wireless sensor and actor network applications is made difficult by the fact that developers have to face up to a set of resource‐constrained devices which have to work wirelessly and distributely. In this work, we propose a framework to create and integrate light databases within nodes that need to manage and process data. The databases will be designed by means of simple entity‐relationship models from which the code needed to manage the database will be generated. Basically, this code will be composed of data structures and the algorithms in charge of managing them. This architecture will help developers avoid data redundancy to better manage the memory of the nodes and to save time in developing applications. Copyright © 2012 John Wiley & Sons, Ltd.

In recent times real-time distributed systems have definitively become peer-to-peer organized. The common interactions are those of different real-time components dealing with sensors or actuators, implementing controllers, performing monitoring and surveillance tasks, and interacting between them in a dynamic decentralized way. There is a need for mechanisms that allow the integration of these independent components, saving development time while keeping their real-time capability. Services and events, thanks to their decoupled nature are perfect candidates for supporting these architectures. The data centric approach goes even farther, introducing a global data space that allows a flexible, decoupled and scalable coordination environment over which services and events can be added as specific interactions mechanisms inside this global data space, in order to support all the architectural possibilities. The Data Distribution Service specification provides a totally decentralized data-centric approach with real-time quality of service support. It is a perfect base upon which to develop a framework for the integration of real-time distributed architectures.

SBASCO provides a new programming model for parallel and distributed numerical applications which exploits the combination of software components and skeletons. This paper presents an extension to both the model and implementation of SBASCO, so that the notion of aspect is applied in conjunction with the original paradigms. The objective is to achieve a higher level of modularity and reuse in parallel scientific codes and applications. Our aspects are managed as components which implement the (sequential or parallel) cross-cutting functionality. Aspects interact with the base code by means of connectors that express the cross-cutting nature of the target concerns. The way in which both aspect weaving and advice code execution are managed is critical for preserving the performance of applications. An implementation of the abstractions for distributed memory parallel systems based on MPI is discussed.

Wireless sensor and actor networks (WSANs) constitute an emerging and pervasive technology that is attracting increased interest for a wide range of applications. The increasing functionality of this kind of network makes it necessary to propose tools and methodologies to facilitate software development. This paper proposes a middleware called MWSAN to provide a set of high level services for sensor and actor networks. The middleware meets the component-oriented paradigm and developers can configure it depending on the actor and sensor resources. It takes into account issues such as the network configuration, the quality of service (QoS) and the coordination among actors. MWSAN is specified in UM-RTCOM, a component model oriented to real time systems adapted to WSANs that allows us to take into account real time requirements in the applications. Additionally, we present an implementation model based on RT Java for actors. In order to apply the proposed middleware we describe an example specified in UM-RTCOM and its RT Java implementation

Wireless sensor and actor networks (WSANs) constitute a new pervasive technology. WSANs have two major requirements: coordination mechanisms for both sensor-actor and actor-actor interactions, and real-time communication to perform correct and timely actions. This paper introduces TC-WSANs, a high-level coordination model that addresses these two requirements and facilitates the application programmer task. Our proposal is based on a (hierarchical) architecture of sensor/actor clusters and the use of tuple channels to achieve communication and synchronization among sensors and actors. A tuple channel is a priority queue structure that allows data structures to be communicated both in a one-to-many and many-to-one way, facilitating the data-centric behavior of sensor queries. The characteristics of TC-WSANs and the primitives that it provides for its integration into a computational host language are presented.

Abstract This paper presents a nuclear power plant simulator kernel based on the high‐performance computing‐oriented Common Component Architecture (CCA). The approach takes advantage of both the component‐based software development and the efficient execution of parallel simulation models. The use of components improves the software life cycle and facilitates the development, maintenance and evolution of the simulator kernel, which can be adapted to different execution scenarios. Data dependencies among simulation models are resolved automatically by means of a novel algorithm, releasing the programmer from this tedious task and, as a result, making the development process easier. This work introduces the main features of the simulator kernel, describing concepts and the model on which it is based. Some preliminary results are shown that anticipate the feasibility and suitability of the proposal. Copyright © 2006 John Wiley & Sons, Ltd.

Wireless sensor and actor networks (WSANs) constitute an emerging and pervasive technology that is attracting increased interest for a wide range of applications. WSANs have two major requirements: coordination mechanisms for both sensor-actor and actor-actor interactions, and real-time communication to perform correct and timely actions. Additionally, the development of WSAN applications is notoriously difficult, due to the extreme resource limitations of nodes. This paper introduces a framework to facilitate the task of the application programmer taking into account these special characteristics of WSANs. We propose a real-time component model using light-weight components. In addition, a high-level coordination model based on tuple channels is integrated into the framework including high-level constructs that abstract the details of communication and facilitate the data-centric behavior of sensor queries.

Abstract This paper proposes the application of modern component‐oriented technologies to the development of nuclear power plant simulators. On the one hand, as a significant improvement on previous simulators, the new kernel is based on the Common Component Architecture (CCA). The use of such a high‐performance computing oriented component technology, together with a novel algorithm to automatically resolve simulation data dependencies, allows the efficient execution of both parallel and sequential simulation models. On the other hand, RT‐CORBA is employed in the development of the rest of the applications that comprise the simulator. This real‐time communication middleware not only makes the management of communications easier, but also provides the applications with real‐time capabilities. Software components used in these two ways, simulation models integrating the kernel and distributed applications from which the simulator is comprised, improve the evolution and maintenance of the entire system, as well as promoting code reusability in other projects. Copyright © 2006 John Wiley & Sons, Ltd.

We present a new coordination model and a small set of programming notations for distributed programming, which can be integrated into very different programming languages (imperative, declarative or object-oriented). Together they allow the development of distributed programs in a compositional way, by assembling different independent pieces of (possibly pre-existing and heterogeneous) code. This approach is similar to many other proposals such an Linda, PCN, CC++, for example, allowing multiparadigm and multilingual integration, and provides a powerful set of concurrent programming techniques, inherited from Concurrent Logic Languages (CLLs), which can be efficiently implemented in distributed systems. The coordination model is based on logic channels; these evolved from the concept of shared logic variables used in CLLs which, with the same expressive power, can be more efficiently implemented in distributed systems. We introduce this coordination model, giving some illustrative examples to show its expressiveness; some implementation issues are also commented on.