Abstract
Wireless sensor networks (WSNs) are prone to many failures such as hardware failures, software failures, and communication failures. The fault detection in WSNs is a challenging problem due to sensor resources limitation and the variety of deployment field. Furthermore, the detection has to be precise to avoid negative alerts, and rapid to limit loss. The use of machine learning seems to be one of the most convenient solutions for detecting failure in WSNs. In this paper, support vector machines (SVMs) classification method is used for this purpose. Based on statistical learning theory, SVMis used in our context to define a decision function. As a light process in term of required resources, this decision function can be easily executed at cluster heads to detect anomalous sensor. The effectiveness of SVM for fault detection in WSNs is shown through an experimental study, comparing it to latest techniques for the same application.