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Mendoza Bernal, José

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Mendoza Bernal, José
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    Missing Data Imputation with Bayesian Maximum Entropy for Internet of Things Applications
    (IEEE Internet of Things Journal, 2021-11-01) González Vidal, Aurora; Rathore, Punit ; Rao, Aravinda S.; Marimuthu Palaniswami; Skarmeta Gómez, Antonio; Mendoza Bernal, José; Ingeniería de la Información y las Comunicaciones; Facultades de la UMU::Facultad de Informática
    Internet of Things (IoT) enables the seamless integration of sensors, actuators and communication devices for real-time applications. IoT systems require good quality sensor data in order to make real-time decisions. However, values are often missing from the sensor data collected owing to faulty sensors, a loss of data during communication, interference and measurement errors. Considering the spatiotemporal nature of IoT data and the uncertainty of the data collected by sensors, we propose a new framework with which to impute missing values utilizing Bayesian Maximum Entropy (BME) as a convenient means to estimate the missing data from IoT applications. Missing sensor measurements adversely affect the quality of data, and consequently the performance and outcomes of IoT systems. Our proposed framework incorporates BME in order to impute missing values in diverse IoT scenarios by making use of the combination of low- and high-precision sensors. Our approach can incorporate the measurement errors of low- precision sensors as interval quantities along with the high-precision sensor measurements, making it highly suitable for real-time IoT systems. Our framework is robust to variations in data, requires less execution time, and requires only a single input parameter, thus outperforming existing IoT data imputation methods. The experimental results obtained for three IoT datasets demonstrate the superiority of the BME framework as regards accuracy, running time and robustness. The framework can additionally be extended to distributed IoT nodes for the online imputation of missing values.
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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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