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The continuous evolution of the agricultural sector justifies the incorporation and adaptation of the latest technologies. Nowadays, managing crops is possible through Internet-based technologies. Their application allows for the exploitation of information and the development of isolated applications, which, although powerful, create challenges for obtaining scalable predictions throughout the useful life of farms. To address this problem, a data model was defined to improve the management of crop plots in irrigation communities and simultaneously monitor crop needs. Consequently, the objective of this study was to create an open and interoperable platform based on standard interfaces and protocols to enable the integration of heterogeneous sources of information, while ensuring interoperability with other third-party solutions for exchanging and exploiting such information. Standard and open interfaces and protocols form the basis of the platform, thereby unifying all information in a single data model, which facilitates the better use and dissemination of information. The system was fully instantiated in a real prototype in an irrigation community; the software improved water irrigation management for the farmers connected to the platform.

The rapid proliferation of the Internet of Things (IoT) systems has resulted in large volumes of heterogeneous data that are often difficult to access and exploit due to limited interoperability and complex application programming interfaces. Data spaces address these challenges by providing governed environments for secure and semantically interoperable data sharing, commonly relying on standardized interfaces such as the ETSI NGSI-LD API. While powerful, these interfaces are primarily designed for machine-to-machine interaction and remain difficult to use directly by human operators. In this paper, we propose an architecture that enables natural-language access to IoT data stored in a data space by integrating Large Language Models (LLMs) with the Model Context Protocol (MCP). Experimental results using fastMCP and OpenAI API to access a FIWARE-based data space demonstrate that our solution offers accuracy even for prompts that require advanced reasoning.

Like what happened to the Internet of Things (IoT), smart cities have become abundant in our lives as well. One of the smart city definitions commonly used is that smart cities solve city problems to enhance citizens’ life quality and make cities sustainable. From the perspective of information and communication technologies (ICT), we think this can be done by collecting and analyzing data to generate insights. The City Data Hub, which is a standard-based city data platform that has been developed, and a couple of problem-solving examples have been demonstrated. The key elements for smart city platforms have been chosen and they have been included in the core architecture principles and implemented as a platform. It has been proven that standard application programming interfaces (APIs) and common data models with data marketplaces, which are the keys, increase interoperability and guarantee ecosystem extensibility.

Nowadays, there are plenty of data sources generating massive amounts of information that, combined with novel data analytics frameworks, are meant to support optimisation in many application domains. Nonetheless, there are still shortcomings in terms of data discoverability, accessibility and interoperability. Open Data portals have emerged as a shift towards openness and discoverability. However, they do not impose any condition to the data itself, just stipulate how datasets have to be described. Alternatively, the NGSI-LD standard pursues harmonisation in terms of data modelling and accessibility. This paper presents a solution that bridges these two domains (i.e., Open Data portals and NGSI-LD-based data) in order to keep benefiting from the structured description of datasets offered by Open Data portals, while ensuring the interoperability provided by the NGSI-LD standard. Our solution aggregates the data into coherent datasets and generate high-quality descriptions, ensuring comprehensiveness, interoperability and accessibility. The proposed solution has been validated through a real-world implementation that exposes IoT data in NGSI-LD format through the European Data Portal (EDP). Moreover, the results from the Metadata Quality Assessment that the EDP implements, show that the datasets’ descriptions generated achieve excellent ranking in terms of the Findability, Accessibility, Interoperability and Reusability (FAIR) data principles.

A considerable number of Internet of Things deployments are isolated from specific solutions, from devices to data platforms. Standardized data models were proposed to overcome the interoperability gap between these deployments. Next generation service interfaces-linked data (NGSI-LD) is one of the proposed platforms that exploits linked data and proposes an information model and an application programming interface (API) for easy use and standard management of context information. The NGSI-LD information model is based on JSON for Linked Data (JSON-LD) as a serialization format for context information. This efficiently exploits the potential of semantics and linked open data. However, the NGSI-LD graph API and query language are still theoretically defined and limited to some preliminary works. Consequently, current NGSI-LD implementations are mainly based on traditional databases, where the JSON-LD serialization is supported but not exploited owing to the difficulties in defining and implementing new NGSI-LD based Graph APIs. One of the basic solutions is the use of an RDF store for NGSI-LD payloads because these types of databases are well defined and maintained and will not need any added effort for JSON-LD based payloads. However, the main complication at this level is the use of reification to annotate relationships. This study focused on both aspects of exploiting the semantics of NGSI-LD by proposing standardized mapping mechanisms to RDF graphs without reifying JSON-LD payloads and with respect to the NGSI-LD context model and API. Our main proposals highlight that exploiting the RDF store for processing NGSI-LD data semantically is feasible and uncomplicated. We illustrated the proposed mapping approaches with real use-case examples and a possible exploitation of semantic approaches.

In response to the challenges posed by climate change, including extreme weather events, such as heavy rainfall and droughts, the agricultural sector is increasingly seeking solutions for the efficient use of resources, particularly water. Pivotal aspects of smart agriculture include the establishment of weather-independent systems and the implementation of precise monitoring and control of plant growth and environmental conditions. Hydroponic cultivation techniques have emerged as transformative solutions with the potential to reduce water consumption for cultivation and offer a sheltered environment for crops, protecting them from the unpredictable impacts of climate change. However, a significant challenge lies in the frequent need for human intervention to ensure the efficiency and effectiveness of these systems. This paper introduces a novel system with a modular architecture, offering the ability to incorporate new functionalities without necessitating a complete system redesign. The autonomous hydroponic greenhouse, designed and implemented in this study, maintains stable environmental parameters to create an ideal environment for cultivating tomato plants. Actuators, receiving commands from a cloud application situated at the network’s edge, automatically regulate environmental conditions. Decision-making within this application is facilitated by a PID control algorithm, ensuring precision in control commands transmitted through the MQTT protocol and the NGSI-LD message format. The system transitioned from a single virtual machine in the public cloud to edge computing, specifically on a Raspberry Pi 3, to address latency concerns. In this study, we analyzed various delay aspects and network latency to better understand their significance in delays. This transition resulted in a significant reduction in communication latency and a reduction in total service delay, enhancing the system’s real-time responsiveness. The utilization of LoRa communication technology connects IoT devices to a gateway, typically located at the main farm building, addressing the challenge of limited Internet connectivity in remote greenhouse locations. Monitoring data are made accessible to end-users through a smartphone app, offering real-time insights into the greenhouse environment. Furthermore, end-users have the capability to modify system parameters manually and remotely when necessary. This approach not only provides a robust solution to climate-induced challenges but also enhances the efficiency and intelligence of agricultural practices. The transition to digitization poses a significant challenge for farmers. Our proposed system not only represents a step forward toward sustainable and precise agriculture but also serves as a practical demonstrator, providing farmers with a key tool during this crucial digital transition. The demonstrator enables farmers to optimize crop growth and resource management, concretely showcasing the benefits of smart and precise agriculture.

In the era of ever-greater data production and collection, public health research is often limited by the scarcity of data. To improve this, we propose data sharing in the form of Data Spaces, which provide technical, business, and legal conditions for an easier and trustworthy data exchange for all the participants. The data must be described in a commonly understandable way, which can be assured by machine-readable ontologies. We compared the semantic interoperability technologies used in the European Data Spaces initiatives and adopted them in our use case of physical development in children and youth. We propose an ontology describing data from the Analysis of Children’s Development in Slovenia (ACDSi) study in the Resource Description Framework (RDF) format and a corresponding Next Generation Systems Interface-Linked Data (NGSI-LD) data model. For this purpose, we have developed a tool to generate an NGSI-LD data model using information from an ontology in RDF format. The tool builds on the declaration from the standard that the NGSI-LD information model follows the graph structure of RDF, so that such translation is feasible. The source RDF ontology is analyzed using the standardized SPARQL Protocol and RDF Query Language (SPARQL), specifically using Property Path queries. The NGSI-LD data model is generated from the definitions collected in the analysis. The translation has been verified on Smart Applications REFerence (SAREF) ontology SAREF4BLDG and its corresponding Smart Data Models (52 models at the time). The generated artifacts have been tested on a Context Broker reference implementation. The tool supports basic ontology structures, and for it to translate more complex structures, further development is needed.