Diseño y desarrollo de una aplicación web para el etiquetado socio-semántico en el ámbito de la gestión forestal

La generación y publicación de datos forestales en la Web es un factor clave para el correcto funcionamiento de diversas tareas relacionadas directa e indirectamente con los árboles y bosques, como son la gestión y la planificación forestal. Dos de las fuentes de datos forestales principales de España son el Inventario Forestal Nacional y el Mapa Forestal de España, que constituyen inventarios de los bosques españoles y están a disposición de la ciudadanía en forma de Datos Abiertos. Para conseguir la integración de estos conjuntos de datos en la llamada Web de Datos, se utilizan tecnologías semánticas, como es el caso del proyecto europeo Cross-Forest. Por otro lado, existen multitud de sitios web y aplicaciones sociales que fomentan la publicación de datos en la Web relacionados con este sector. La colaboración ciudadana permite dar un paso más allá en la obtención de datos forestales, pues permite obtener y capturar una cantidad de datos superior a la que cualquier institución podría generar por sí sola. Sin embargo, si la publicación de los datos generados por los ciudadanos no se hace con formatos y modelos de datos unificados, la reutilización de esos datos por parte de aplicaciones distintas será muy difícil, favoreciendo la creación de “silos de datos” aislados en la Web. Como solución, en este Trabajo Fin de Grado se propone la creación de una aplicación web de carácter social que haga uso de las tecnologías de la Web Semántica, de manera que los datos publicados en ella sigan una estructura única, bien definida, que permita explotar los datos existentes en la Web de Datos. Además, dicha aplicación trata de fomentar la colaboración ciudadana, de manera que se contribuya a enriquecer la Web de Datos actual con nuevas anotaciones sociales. Esta aplicación, denominada Timber, permitirá a los ciudadanos recuperar y publicar información sobre los árboles de su alrededor disponibles en la Web de Datos. Timber pretende dar cabida a diversos tipos de usuarios (senderistas, profesionales de la gestión forestal, investigadores, estudiantes, aficionados, etc.), permitiéndoles buscar, geolocalizar, fotografiar y describir árboles de manera compartida, amigable y entretenida.The generation and publication of forestry data on the Web is a key factor for the proper functioning of various tasks directly and indirectly related to trees and forests, such as forest management and planning. Two of the main sources of forest data in Spain are the National Forest Inventory and the Forest Map of Spain, which are inventories of Spanish forests and are available to the public in the form of Open Data. To achieve the integration of these data sets in the so-called Web of Data, semantic technologies are used, as is the case of the european Cross-Forest project. On the other hand, there are many websites and social applications that promote the publication of data on the Web related to this sector. Community collaboration allows to go one step further in obtaining forest data, so that it allows to obtain and capture a larger amount of data than any institution could generate on its own. However, if the publication of user-generated data is not done with unified data formats and models, the reuse of such data by different applications will be very difficult, favouring the creation of isolated ’data silos’ on the Web. As a solution, this Project proposes the creation of a social web application that makes use of Semantic Web technologies, so that the data published on it follows a unique, well-defined structure that allows the exploitation of existing data on the Web of Data. Furthermore, this application tries to promote community collaboration, so that it contributes to enrich the current Web of Data with new social annotations. This application, called Timber, allows citizens to retrieve and publish information about the trees around them available in the Web of Data. Timber aims to accommodate various types of users (hikers, forest management professionals, researchers, students, amateurs, etc.), allowing them to search, geolocate, photograph and describe trees in a shared, friendly and entertaining way.Grado en Ingeniería de Tecnologías de Telecomunicació

Infrastructure-wide and intent-based networking dataset for 5G-and-beyond AI-driven autonomous networks

In the era of Autonomous Networks (ANs), artificial intelligence (AI) plays a crucial role for their development in cellular networks, especially in 5G-and-beyond networks. The availability of high-quality networking datasets is one of the essential aspects for creating data-driven algorithms in network management and optimisation tasks. These datasets serve as the foundation for empowering AI algorithms to make informed decisions and optimise network resources efficiently. In this research work, we propose the IW-IB-5GNET networking dataset: an infrastructure-wide and intent-based dataset that is intended to be of use in research and development of network management and optimisation solutions in 5G-and-beyond networks. It is infrastructure wide due to the fact that the dataset includes information from all layers of the 5G network. It is also intent based as it is initiated based on predefined user intents. The proposed dataset has been generated in an emulated 5G network, with a wide deployment of network sensors for its creation. The IW-IB-5GNET dataset is promising to facilitate the development of autonomous and intelligent network management solutions that enhance network performance and optimisation

NetLabeller:architecture with data extraction and labelling framework for beyond 5G networks

The next generation of network capabilities coupled with artificial intelligence (AI) can provide innovative solutions for network control and self-optimisation. Network control demands a detailed knowledge of the network components to enforce the correct control rules. To this end, an immense number of metrics related to devices, flows, network rules, etc. can be used to describe the state of the network and to gain insights about which rule to enforce depending on the context. However, selection of the most relevant metrics often proves challenging and there is no readily available tool that can facilitate the dataset extraction and labelling for AI model training. This research work therefore first develops an analysis of the most relevant metrics in terms of network control to create a training dataset for future AI development purposes. It then presents a new architecture to allow the extraction of these metrics from a 5G network with a novel dataset visualisation and labelling tool to help perform the exploratory analysis and the labelling process of the resultant dataset. It is expected that the proposed architecture and its associated tools would significantly speed up the training process, which is crucial for the data-driven approach in developing AI-based network control capabilities

Diseño y desarrollo de EducaWood: un sistema web socio-semántico para la educación medioambiental

En España se recogen y mantienen de forma oficial datos forestales que son publicados como datos abiertos. Estos datos abiertos pueden ser reutilizados en aplicaciones que permitan su visualización y utilización para fomentar actividades que involucren la educación medioambiental. Para favorecer la integración de estos datos se utilizan tecnologías semánticas, tarea realizada exitosamente en el proyecto europeo Cross-Forest, a partir del cual los datos del Inventario Forestal Nacional (IFN) y del Mapa Forestal Español (MFE) están disponibles como datos abiertos enlazados (LOD). Más allá del esfuerzo de las administraciones públicas, aproximaciones como la "ciencia ciudadana" permiten aumentar enormemente el número de personas involucradas en el enriquecimiento de las bases de datos forestales existentes. En el presente Trabajo Fin de Máster se presenta el diseño y desarrollo de EducaWood, un portal web socio-semántico que permite explorar la información forestal de una zona del territorio español proveniente de los datos del Cross-Forest y enriquecerla con anotaciones de árboles. EducaWood proporciona una interfaz de usuario de tipo formulario con la que pueden realizarse anotaciones semánticas, de manera que los usuarios no necesitan conocimiento de las tecnologías semánticas subyacentes (RDF y SPARQL). El principal objetivo de EducaWood es impulsar las actividades de aprendizaje medioambiental, uno de los aspectos principales de la “Educación para los Objetivos de Desarrollo Sostenible” de la UNESCO, que forma parte de la Agenda 2030 del Gobierno español. Con el uso de EducaWood, el profesorado puede proponer actividades de aprendizaje medioambiental que los estudiantes realizan de manera presencial u online (a través de visitas virtuales al campo). Los datos generados por los alumnos son enriquecidos con otras fuentes de datos abiertos como el MFE, el IFN o DBPedia. EducaWood ayuda al alumnado a conocer mejor su entorno, a la vez que se promociona la toma de conciencia ecológica.Forestry data are collected and maintained officially in Spain and published as open data. This open data can be reused in applications that allow its visualisation and use to promote activities involving environmental education. Semantic technologies are used to facilitate the integration of these data, a task successfully carried out in the European Cross-Forest project, from which data from the National Forest Inventory (IFN) and the Spanish Forest Map (MFE) are available as linked open data (LOD). Beyond the efforts of public administrations, approaches such as "citizen science" allow for an enormous increase in the number of people involved in the enrichment of existing forest datasets. This Master Thesis presents the design and development of EducaWood, a socio-semantic web portal that allows to explore the forest information of an area of the Spanish territory from the Cross-Forest data and to enrich it with tree annotations. EducaWood provides a form-like user interface with which semantic annotations can be made, so that users do not need knowledge of the underlying semantic technologies (RDF and SPARQL). The main objective of EducaWood is to promote environmental learning activities, one of the main aspects of UNESCO’s "Education for Sustainable Development Goals", which is part of the Spanish Government’s Agenda 2030. Using EducaWood, teachers can propose environmental learning activities that students carry out in-person or online (through virtual field visits). The data generated by the students are enriched with other open data sources such as MFE, IFN or DBPedia. EducaWood helps students to learn more about their environment, while promoting ecological awareness.Departamento de Teoría de la Señal y Comunicaciones e Ingeniería TelemáticaMáster en Ingeniería de Telecomunicació

Multi-layer multi-technology firewall optimisation in beyond 5G networks using machine learning classifiers

Enhancing the security of Beyond 5G (B5G) and Pre-6G networks poses significant challenges, particularly in effectively implementing firewalls. Within a wide range of technologies aimed at implementing mitigation mechanisms, achieving optimal technology selection and rule set configuration within these diverse technologies is immensely complex. In addition, these rules are usually based on pre-configured template and lack of optimisation with information of real-time network status, often resulting in sub-optimal configurations. In this paper, an architecture that enables the optimisation of multi-layer multi-technology firewalls integrated in a B5G network testbed is presented. Our proposed framework supports network control monitoring and automatic deployment of firewall rules in three different virtual function implementations: iptables, Open vSwitch and Linux traffic control. After performing a comparison among four popular machine learning (ML) models for the optimal selection, our results show that Random Forest is the best algorithm for the proposed solution with a F1-score of 0.9083

Handling imbalanced 5G and beyond network tabular data using conditional generative models

Data-driven machine learning based approaches have been playing increasingly important roles in 5G and beyond (B5G) network management and optimisation. One of the major challenges is that the existence of class imbalance in networking datasets can greatly bias the classifier towards a majority classification. This discrepancy can pose a serious problem for AI-based models, which require large and diverse amounts of data to learn patterns and generate classifications. The generation of synthetic data is a process that has evolved over time from the application of statistical models to a more machine learning-centric approaches. In this research work we present the development, implementation, evaluation and comparison of four generative models for tabular data on a B5G network management dataset. These models have been previously optimised according to certain evaluation metrics of the generated synthetic data. The dataset presents a problem of imbalance between its classes, which is improved by using generative models to enrich it with synthetic data. The results show that machine learning based generative models obtain more accurate data than traditional statistical models, and are much faster in terms of conditional data sampling

European Conference on Technology Enhanced Learning, EC-TEL 2021 (16º. 2021. Bolzano, Italy)

Producción CientíficaEducawood is a socio-semantic annotation system intended for environmental learning in Secondary and Higher Education. It can be used to socially annotate trees and other ecosystem structures such as dead wood. Furthermore, Educawood allows the exploration of existing semantic datasets of land cover maps and forestry inventories as well as social tree annotations (all released as Linked Open Data). Teachers can browse these data to propose contextualized environmental education activities, e.g. finding and annotating singular trees. Students can go on a field trip and use Educawood with their mobile devices to submit tree annotations. Follow-up activities can exploit socially-created tree annotations, for example in virtual field trips.Junta de Castilla y León - Fondo Europeo de Desarrollo Regional (project VA257P18)Agencia Estatal de Investigación - Fondo Europeo de Desarrollo Regional (project TIN2017-85179-C3-2-R)Cross-Forest (project CEF 2017-EU-IA-0140)VirtualForests (project Erasmus+ 2020-1-ES01- KA226-HE-095836)Universidad de Valladolid (project UVA-PID2020-015