Abstract The W3C Web of Things (WoT) is a leading technology that facilitates dynamic information management in the Internet of Things (IoT). In most IoT scenarios, devices and their associated information change continuously, generating a large amount of data. Hence, to correctly use the information and the data generated by different devices, a new perspective of managing and ensuring data quality is recommended. Applying Data Science techniques to create the data model can help to manage and ensure data quality by creating a common schema that can be reused in future projects, as well as producing recommendations to facilitate Service Discovery. In addition, due to the dynamic devices that change over time or under specific circumstances, the data model created must be sufficiently abstract to add new instances and to support new requirements that devices should incorporate. The use of models helps to raise the abstraction level, adapting it to the continuous changes of devices by defining instances associated with the data model. This paper proposes two data models: one for Cyber‐Physical Systems (CPS) to define device information fetched by a Discovery Service, and another for applying Deep Learning in natural language problems through a Transformer approach. The latter matches user queries in natural language sentences with WoT devices or services. These data models expand the Thing Description model to help find similar CPSs by giving a confidence level to each CPS based on features such as security and the number of times the device was accessed. The results show how the proposed models support the search process of CPSs in syntactic and natural language searches. Furthermore, the four levels of the FAIR principles are validated for the proposed data models, thus ensuring the data's transparency, reproducibility, and reusability.

IoT devices, as a result of technological advancements, may have different ways of operating and communicating despite having the same features. Therefore, finding a specific device among the whole of deployed devices can be a difficult task. To help find devices in an efficient and timely way, we propose a recommender system using deep learning for matching W3C Web of Things artifacts (called as WoT devices) with natural language queries. The proposal uses the Transformer algorithm to study the usage of deep learning to facilitate searching for devices, assuming that the model can be used as a recommendation tool to match WoT devices in Cyber-Physical Systems.

Systems based on computer vision and artificial intelligence are an alternative for repetitive inspection processes. However, it is possible to extend the learning capacity of these systems to classify multiple objects using edge computing. This allows combining local processing with cloud processing to expand the possibilities and reduce the response time. In this work, a classification architecture based on remote web services and local neural networks is proposed. To test this architecture, Microsoft Azure cognitive web services and its Computer Vision API have been used, combined with the use of transfer learning and ResNet 50. The cloud service allows the identification and labelling of image content, while the Edge service, based on the neural network, allows the generation of classification models for those objects not identified or incorrectly identified by the remote service. The architecture allows to extend the possibility of image recognition by integrating web services that combined with edge processing accelerate the identification process. The proposed architecture is composed of three layers; (a) a physical layer, for the mechanical and electronic structure; (b) a logical layer, which defines the interaction of the remote and local image recognition web services, and (c) an application layer, for the integration of the monitoring and control interfaces. Finally, the architecture was evaluated through functionality testing and performance metrics of classification models, as well as load and usability testing.

The Internet of Things (IoT) is composed of many heterogeneous pieces. The Web of Things (WoT) was created to establish a common device definition language, the Thing Description (TD), that would allow developers to set an interaction mechanism without understanding the different device implementations. Besides a definition language, a WoT ecosystem must be characterized by a robust discovery system in which the other Thing Descriptions of the devices can be easily located, registered and consulted. According to these two pillars (description and discovering), this article presents a Discovery Service model for the WoT, capable of working proactively, searching for IoT devices compatible with the Thing Description, and passively being able to register, query or list different devices when the client chooses. Furthermore, Digital Dice is used in the proposal, a software abstraction of IoT devices for managing devices, adding some features and taking advantage of microservices architectures. Finally, in the recent updates and modifications over the WoT recommendations, one of the main problems researchers may face is that there are few public domain implementations. This article also presents a fully interactive solution using the Discovery Service and Digital Dice to address this problem.

Within the smart solutions (Smart Industry, Smart Home and Smart City), the concept of Smart Cities is the one having the greatest growth, inducing an increment in the number of developers focused on carrying out this idea, and growth in the number of companies that contribute with solutions like IoT platforms. These platforms help in the development of smart solutions providing tools and services that automate some of the development processes and ease the creation of software for smart environments. To support the growth of smart cities, we propose MI-FIWARE, an architecture based on microservices (MI) and the IoT platform FIWARE. MI-FIWARE reduces developers from the back-end workload and the processing of context information, in a way that only the front-side has to be developed using a web component template proposed. MI- FIWARE architecture is based on microservices, taking advantage of FIWARE as a modular platform and establishing the possibility of using microservices to add extra functionality to the IoT platform, helping the developers in other development processes like security.

The communication between different IoT systems presents a series of challenges in the processing and analysis of the heterogeneous information managed by said systems. The W3C Web of Things (WoT) standard offers a common model to define those devices following the guidelines established by the Thing Description. One of the caveats of the WoT is that the Thing Description does not define behaviour, so there is no way to know if an interaction is the consequence of another one. Each IoT device is represented using a Thing Description as an individual entity by design. In this paper, we propose a mechanism to improve interoperability between devices, laying the foundations on how to establish a relation linking them. This relation is formed by two different entities: the "causes" and the "effects". These two entities are at the same time interactions affordances provided by the Thing Description schema of each thing involved. Cause-effect relations help to define how devices work with each other following an event communication strategy.

The Web of Things proposes an IoT device definition framework, the Thing Description. Conceptually, this definition covers a wide range of devices, protocols, and data types. On the other hand, the proliferation of IoT devices and the traits that they usually exhibit, such as limited number of resources, leads to the emergence of bottlenecks that cause the slowdown of requests and even the shutdown of devices. To solve these problems, we introduce the concept of Digital Dice, a digital representation of an IoT device based on microservices that uses the framework established by the W3C Web of Things. This article describes WoTnectivity, a mechanism that allows the connection of multiple protocols on IoT devices by means of several common communication patterns. Besides, establishing the possibility of connecting with different protocols, WoTnectivity has been developed with future expansion in mind, thus an explanation on how to add more protocols is also studied in this paper.

Mashup user interfaces provides their functionality through the combination of different services. The integration of such services can be solved by using reusable and third-party components. Furthermore, these interfaces must be adapted to user preferences, context changes, user interactions and c omponent availability. Model transformation is a useful mechanism to address this adaptation but normally these operations only focus on the functional requirements. In this sense, quality attributes should be included in the adaptation process to obtain the best adapted mashup user interface. This paper proposes a generic quality-aware transformation process to support the adaptation of software architectures. The transformation process has been applied in ENIA, a geographic information system, by constructing a specific quality model for the adaptation of mashup user interfaces. This model is taken into account for evaluating the different transformation alternatives and choosing the one that maximizes the quality assessments. The approach has been validated by a set of adaptation scenarios that are intended to maximize different quality factors and therefore apply distinct combinations of metrics.

Summary One of the great challenges the information society faces is dealing with the huge amount of information generated and handled daily on the Internet. Today, progress in Big data proposals attempt to solve this problem, but there are certain limitations to information search and retrieval due basically to the large volumes handled, the heterogeneity of the information, and its dispersion among a multitude of sources. In this article, a formal framework is defined to facilitate the design and development of an environmental management information system, which works with a heterogeneous and large amount of data. Nevertheless, this framework can be applied to other information systems that work with Big data, because it does not depend on the type of data and can be utilized in other domains. The framework is based on an ontological web‐trading model (OntoTrader), which follows model‐driven engineering and ontology‐driven engineering guidelines to separate the system architecture from its implementation. The proposal is accompanied by a case study, SOLERES‐KRS, an environmental knowledge representation system designed and developed using software agents and multi‐agent systems. Copyright © 2017 John Wiley & Sons, Ltd.

Summary Graphical user interfaces are not always developed for remaining static. There are GUIs with the need of implementing some variability mechanisms. Component‐based GUIs are an ideal target for incorporating this kind of operations, because they can adapt their functionality at run‐time when their structure is updated by adding or removing components or by modifying the relationships between them. Mashup user interfaces are a good example of this type of GUI, and they allow to combine services through the assembly of graphical components. We intend to adapt component‐based user interfaces for obtaining smart user interfaces. With this goal, our proposal attempts to adapt abstract component‐based architectures by using model transformation. Our aim is to generate at run‐time a dynamic model transformation, because the rules describing their behavior are not pre‐set but are selected from a repository depending on the context. The proposal describes an adaptation schema based on model transformation providing a solution to this dynamic transformation. Context information is processed to select at run‐time a rule subset from a repository. Selected rules are used to generate, through a higher‐order transformation, the dynamic model transformation. This approach has been tested through a case study which applies different repositories to the same architecture and context. Moreover, a web tool has been developed for validation and demonstration of its applicability. The novelty of our proposal arises from the adaptation schema that creates a non pre‐set transformation, which enables the dynamic adaptation of component‐based architectures. Copyright © 2014 Copyright © 2014 John Wiley & Sons, Ltd.

Nowadays, large part of the efforts in software development are focused on achieving systems with an as high as possible level of adaptation. With the traditional technique of model-driven development this can be largely accomplished. The inconvenience of these techniques however, is that the models are usually manipulated at design-time by means of fixed transformation. Furthermore, the transformations that manipulate these models cannot change dynamically according to the current execution context. This paper presents a transformation pattern aimed to adapt architectural models at runtime, this means that these models may change dynamically at runtime. The transformations that produce this model adaptation are not fixed, but dynamically composed by selecting the most appropriate set of rules from those available in a repository. As an example scenario for the application of these transformations, we chose architectural models representing component-based UIs.

Current open information systems need formal semantic definitions to describe and handle information which is dealt within different interaction environments such as human-to-human, human-to-computer, and computer-to-computer interactions. This paper provides a semantic definition of the knowledge used by environmental management information systems. We describe an ontological model in UML class diagrams and present a formalization of the ontology in first-oder logic and encoded in description logic. A model transformation approach is also used to map the UML class diagram elements into web ontology language. A running example is presented to explain the ontological knowledge modeling of EMIS SOLERES cartography and satellite information.