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.

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.