Journal article

Missing Data Imputation with Bayesian Maximum Entropy for Internet of Things Applications

Aurora Gonzalez-Vidal, Punit Rathore, Aravinda S Rao, Jose Mendoza-Bernal, Marimuthu Palaniswami, Antonio F Skarmeta-Gomez

IEEE Internet of Things Journal | Institute of Electrical and Electronics Engineers (IEEE) | Published : 2020


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..

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