A machine learning methods: Outlier detection in WSN

Abstract

Wireless sensor networks are gaining more and more attention these days. They gave us the chance of collecting data from noisy environment. So it becomes possible to obtain precise and continuous monitoring of different phenomenons. However wireless Sensor Network (WSN) is affected by many anomalies that occur due to software or hardware problems. So various protocols are developed in order to detect and localize faults then distinguish the faulty node from the right one. In this paper we are concentrated on a specific type of faults in WSN which is the outlier. We are focus on the classification of data (outlier and normal) using three different methods of machine learning then we compare between them. These methods are validated using real data obtained from motes deployed in an actual living lab.