Data Imputation in Wireless Sensor Networks Using a Machine Learning-Based Virtual Sensor

Abstract

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 which efficiently reached an optimal solution for each sensor node. The system was able to successfully identify and replace physical sensor nodes that were disconnected from the network with corresponding virtual sensors. The virtual sensors imputed values with very high accuracies when compared to the physical sensor values.