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Data Imputation in Wireless Sensor Networks Using a Machine Learning-Based Virtual Sensor
2020
Journal of Sensor and Actuator Networks
Data integrity in wireless sensor networks (WSN) is very important because incorrect or missing values could result in the system making suboptimal or catastrophic decisions. Data imputation allows for a system to counteract the effect of data loss by substituting faulty or missing sensor values with system-defined virtual values. This paper proposes a virtual sensor system that uses multi-layer perceptrons (MLP) to impute sensor values in a WSN. The MLP was trained using a genetic algorithm
doi:10.3390/jsan9020025
fatcat:4xifoaih25ewnhubgu5x47se2y