An Efficient Outlier Detection and Classification Clustering-Based Approach for WSN

Wireless Sensor Network (WSN) is one of the main components of the Internet of things (IoT) for gathering information and monitoring the environment in a variety of applications (medical, agricultural, manufacturing, militarily, etc.). However, data collected and transferred from sensors to the base station are susceptible to have outliers. These outliers can occur due to sensor nodes itself or to the harsh environment where they are deployed. Thus, it is necessary for the WSN to be able to detect the outliers and take actions in order to ensure network quality of service (in terms of reliability, latency, etc.) and to avoid further degradation of the application efficiency. In this paper, we propose a distributed outlier detection and classification algorithm for WSN. Our approach is capable of distinguish between an error due to a faulty sensor and an error due to an interesting event. We take into consideration the spatial-temporal correlation between sensors' data values and between neighbouring sensor nodes. Simulations with both synthetic and real datasets showed that our proposed approach outperforms other techniques by obtaining high Detection Rate (DR) and low False Alarm Rate (FAR).