Assessing data quality in a sensor network for environmental monitoring

Assessing the quality of sensor data in environmental monitoring applications is important, as erroneous readings produced by malfunctioning sensors, calibration drift, and problematic climatic conditions such as icing or dust, are common. Traditional data quality checking and correction is a painstaking manual process, so the development of automatic systems for this task is highly desirable. This study investigates machine learning methods to identify and clean incorrect data from a real-world environmental sensor network, the Jornada Experimental Range, located in Southern New Mexico. We analyze several learning algorithms and data replacement schemes and conclude that learning algorithms are an effective way of cleansing this type of datasets.