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 : 2021
Abstract
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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Awarded by Australian Research Council
Funding Acknowledgements
This work was supported in part by the Ministry of Economy and Competitiveness through the PERSEIDES Project under Grant TIN2017-86885-R, in part by the European Commission through the H2020 IoTCrawler under Contract 779852 and DEMETER under Agreement 857202 EU Projects, in part by the European Social Fund and Youth European Initiative through the Spanish Seneca Foundation, and in part by the Australian Research Council Discovery Project under Grant DP190102828.