Abstract
Wireless Sensor Network is composed of small, low cost, low energy, and multifunctional sensors. In addition, this network could have scalability, topology, synchronization, radio-coverage, safety and security constraints . Therefore, our challenge is to classify data into normal and abnormal measurements using outlier detection methods. This paper explore the density-based method Ordering Points to Identify the Clustering Structure. Proposed detector applies an auto- configuration of parameters without previous known environmental conditions. It also extracts hierarchical clusters that serve in a post-processing treatment for classification of data into errors and events. Performance is examined within a real and synthetic databases from Intel Berkeley Research lab. Results demonstrate that our proposed process analyzes data of this network with an average equal to 81% of outlier detection rate, 74% of precision rate and only 2% of false alarms rate that it is very low compared to other methods.