Outlier Detection in Wireless Sensor Networks using Machine Learning Techniques: A Survey

Abstract

Now-a-days, Internet of Things (IoT) based systems are developing very fast which have various type of wireless sensor networks (WSN) behind it. These networks have various applications viz., healthcare, agricultural, industrial and military applications. Anomaly or outlier detection is one of the important research problems in such applications of wireless sensor networks where a huge amount of data is collected. Anomaly detection helps to find out defective, erroneous, and noisy nodes. There are many techniques which are used to detect the anomalies. Machine learning algorithm (MLA) based approaches are very much useful and effective among them and provides better accuracy. This paper presents a brief study on such approaches.