Ensemble based sensing anomaly detection in wireless sensor networks

Highlights? We developed an efficient ensemble of five classifiers to detect sensor anomalies. ? We model the correct sensor behavior in complex/dynamic environments using five models. ? We solve the problems related to training, testing and evaluation of the ensemble. ? Our ensemble offers reliable estimations to replace the erroneous measurements. ? We present a case study to show the preciseness of our method. Wireless sensor networks are often used to monitor and measure physical characteristics from remote and sometimes hostile environments. In these circumstances the sensing data accuracy is a crucial attribute for the way these applications complete their objectives, requiring efficient solutions to discover sensor anomalies. Such solutions are hard to be found mainly because the intricate defining of the correct sensor behavior in a complex and dynamic environment. This paper tackles the sensing anomaly detection from a new perspective by modeling the correct operation of sensors not by one, but by five different dynamical models, acting synergically to provide a reliable solution. Our methodology relies on an ensemble based system composed of a set of diverse binary classifiers, adequately selected, to implement a complex decisional system on network base station. Moreover, every time a sensing anomaly is discovered, our ensemble offers a reliable estimation to replace the erroneous measurement provided by sensor.