Passive Diagnosis for WSNs Using Data Traces

Abstract

Diagnosis for wireless sensor networks is difficult, due to the limited resources and the ad hoc manner of networks. The existing approaches mainly focus on collecting diagnosis metrics, which bring heavy communication overhead to the network. We present a new model called DSD for network diagnosis which deduce the root causes for failures using the sensing data traces. We discover that the characteristics of the sensing data reflect the network status in some way, according to considerable experiments in the GreenOrbs project. We mine the relationships between the sensing data and the failures in the sensor networks, and record them in a failure knowledge library. Through this diagnosis mechanism, we deduce the root cause of the failures without adding any additional network burden. Moreover, the failure knowledge library can be used to improve the efficiency of diagnosis. We analyze the three months sensing data from the GreenOrbs project, and experimental results show that the proposed scheme can improve the diagnosis performance with low energy cost.