Browsing by Subject "IoT"

Now showing 1 - 6 of 6
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    A Methodology Based on Machine Learning and Soft Computing to Design More Sustainable Agriculture Systems
    (MDPI, 2023) Cadenas Figuerero, J.M.; Garrido Carrera, María del Carmen; Martínez España, R.; Ingeniería de la Información y las Comunicaciones
    Advances in new technologies are allowing any field of real life to benefit from using these ones. Among of them, we can highlight the IoT ecosystem making available large amounts of information, cloud computing allowing large computational capacities, and Machine Learning techniques together with the Soft Computing framework to incorporate intelligence. They constitute a powerful set of tools that allow us to define Decision Support Systems that improve decisions in a wide range of real-life problems. In this paper, we focus on the agricultural sector and the issue of sustainability. We propose a methodology that, starting from times series data provided by the IoT ecosystem, a preprocessing and modelling of the data based on machine learning techniques is carried out within the framework of Soft Computing. The obtained model will be able to carry out inferences in a given prediction horizon that allow the development of Decision Support Systems that can help the farmer. By way of illustration, the proposed methodology is applied to the specific problem of early frost prediction. With some specific scenarios validated by expert farmers in an agricultural cooperative, the benefits of the methodology are illustrated. The evaluation and validation show the effectiveness of the proposal.
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    A Transfer Learning Framework for predictive energy-related scenarios in Smart Buildings
    (IEEE Transactions on Industry Applications, 2023-02-01) González Vidal, Aurora; Niu, Shuteng; Song, Houbing; Skarmeta Gómez, Antonio; Mendoza Bernal, José; Ingeniería de la Información y las Comunicaciones; Facultades de la UMU::Facultad de Informática
    Human activities and city routines follow patterns. Transfer learning can help achieve scalable solutions towards the realisation of smart cities accounting for similarities between regions, domains, and activities. In this study, we propose a Transfer Learning-based framework for smart buildings to test this hypothesis in energy-related problems. Our framework has two major components: the network creation and the transferable predictive model. In order to create the network that groups buildings sharing characteristics, we evaluated two strategies: a novel clustering algorithm for mixed data, k-prod, and clustering the image-based representation of time series. Then, a combination of Long Short Term Memory and Convolutional Neural Network was trained on the centroids of the clusters for energy consumption prediction. The Coefficient of Variation of the Root Mean Squared Error (CVRMSE) of the predictions in such clusters vary between 3.85 and 58.85 %. The obtained parameters were transferred to the rest of the buildings for predictive purposes, finding accurate results in buildings with little data. Our framework deals with insufficient training data since parameters from scenarios with more sensors can be received. It also carries out state-of-the-art performance on 3 datasets from different sources having in total 533 rooms/buildings and two energy efficiency domains: consumption prediction reducing the CVRMSE in a 21.6 %, and air conditioning usage prediction moving from a 4.18 % to a 0.28% CVRMSE. Our framework extracts more knowledge from available IoT deployments, so that smartness could be spread between environments at a fewer cost given that less individual effort will be needed.
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    CORECONF implementation as SDN southboundInterface for IoT: an OSCORE/EDHOC use case
    (Institute of Electrical and Electronics Engineers Inc., 2025) Fernández, Javier A.; Marín López, Rafael; López Millán, Gabriel; Toutain, Laurent; Ingeniería de la Información y las Comunicaciones; Facultades de la UMU::Facultad de Informática
    The Internet of Things (IoT) aims to gather valuable data from our surroundings through resource-constrained networks and devices. For this reason, efficient and lightweight communication protocols are required to be developed and adopted. CORECONF, a network management protocol designed for constrained environments, provides a promising solution for IoT device configuration. This work introduces pycoreconf, an open-source implementation of CORECONF, with the goal of testing the protocol and making it more accessible to researchers and developers by enabling its use in real-world scenarios and experimental setups. In this paper, we evaluate its performance and applicability as a southbound interface in an SDN-based architecture, demonstrating its potential for configuring security contexts between IoT devices. Potential for other use cases remains to be explored in future work. Our results suggest that pycoreconf is a viable tool for those interested in exploring and adopting CORECONF in IoT scenarios.
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    Open Access
    ¿Están preparados nuestros profesionales para la web de las cosas?
    (Ediciones Profesional de la Información, 2019-10-03) Pastor Sánchez, Juan Antonio; Información y Documentación
    Se aborda el papel que tendrá la web de las cosas en el contexto de la internet de las cosas en general y el despliegue de las tecnologías basadas en el 5G. Se analizan las ventajas que aportaría la web de las cosas, así como la necesidad de adoptar una arquitectura estándar. Se trata también el papel que los profesionales de la información deben adoptar ante la web de las cosas y la necesaria evolución de su perfil y competencias laborales hacia la gestión de datos. Finalmente se hace una reflexión sobre si en España se está haciendo una adecuada formación para que esos profesionales alcancen las competencias adecuadas.
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    Evaporation Forecasting through Interpretable Data Analysis Techniques
    (MDPI, 2022) Garrido Carrera, María del Carmen; Cadenas Figueredo, J.M.; Bueno-Crespo, A.; Martínez España, R.; Giménez, J.G.; Cecilia, J.M.; Ingeniería de la Información y las Comunicaciones; Ingeniería y Tecnología de Computadores
    Climate change is increasing temperatures and causing periods of water scarcity in arid and semi-arid climates. The agricultural sector is one of the most affected by these changes, having to optimise scarce water resources. An important phenomenon within the water cycle is the evaporation from water reservoirs, which implies a considerable amount of water lost during warmer periods of the year. Indeed, evaporation rate forecasting can help farmers grow crops more sustainably by managing water resources more efficiently in the context of precision agriculture. In this work, we expose an interpretable machine learning approach, based on a multivariate decision tree, to forecast the evaporation rate on a daily basis using data from an Internet of Things (IoT) infrastructure, which is deployed on a real irrigated plot located in Murcia (southeastern Spain). The climate data collected feed the models that provide a forecast of evaporation and a summary of the parameters involved in this process. Finally, the results of the interpretable presented model are validated with the best literature models for evaporation rate prediction, i.e., Artificial Neural Networks, obtaining results very similar to those obtained for them, reaching up to 0.85R2 and 0.6MAE. Therefore, in this work, a double objective is faced: to maintain the performance obtained by the models most frequently used in the problem while maintaining the interpretability of the knowledge captured in it, which allows better understanding the problem and carrying out appropriate actions.
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    Providing personalized energy management and awareness services for energy efficiency in smart buildings
    (MDPI, 2017-09-07) Fotopoulou, Eleni; Zafeiropoulos, Anastasios; Simsek, Umutcan; González Vidal, Aurora; Tsiolis, George; Gouvas, Panagiotis; Liapis, Paris; Fensel, Anna; Skarmeta Gómez, Antonio; Terroso Sáenz, Fernando; Ingeniería de la Información y las Comunicaciones; Facultades de la UMU::Facultad de Informática
    Considering that the largest part of end-use energy consumption worldwide is associated with the buildings sector, there is an inherent need for the conceptualization, specification, implementation, and instantiation of novel solutions in smart buildings, able to achieve significant reductions in energy consumption through the adoption of energy efficient techniques and the active engagement of the occupants. Towards the design of such solutions, the identification of the main energy consuming factors, trends, and patterns, along with the appropriate modeling and understanding of the occupants’ behavior and the potential for the adoption of environmentally-friendly lifestyle changes have to be realized. In the current article, an innovative energy-aware information technology (IT) ecosystem is presented, aiming to support the design and development of novel personalized energy management and awareness services that can lead to occupants’ behavioral change towards actions that can have a positive impact on energy efficiency. Novel information and communication technologies (ICT) are exploited towards this direction, related mainly to the evolution of the Internet of Things (IoT), data modeling, management and fusion, big data analytics, and personalized recommendation mechanisms. The combination of such technologies has resulted in an open and extensible architectural approach able to exploit in a homogeneous, efficient and scalable way the vast amount of energy, environmental, and behavioral data collected in energy efficiency campaigns and lead to the design of energy management and awareness services targeted to the occupants’ lifestyles. The overall layered architectural approach is detailed, including design and instantiation aspects based on the selection of set of available technologies and tools. Initial results from the usage of the proposed energy aware IT ecosystem in a pilot site at the University of Murcia are presented along with a set of identified open issues for future research.