Sensor data modeling and validating for wireless soil sensor network

Abstract

Precise modeling of field sensor data is an important link in precision agriculture which uses a wireless network for data collecting and field management. A good sensor model allows accurate prediction of environmental variables even with incomplete sensor data and provides basis to assess the quality of sensor readings. We investigate a clustered sensor model using observations of nearby sensors. The proposed method uses a cluster of self-evolving sub-models to model the dynamic and correlation between the networked field sensors. Each cluster represents a set of closely-related sensor attributes. The model is shown to produce accurate sensor prediction when proper attributes are selected during model training. The clustered sensor model is evaluated using field data collected in a high tunnel greenhouse. Our experiment data indicate that correlation of sensor attributes can be identified from training data and significantly improve prediction accuracy with the presence of faulty sensor data.