Outlier detection for wireless sensor networks using density-based clustering approach
Outlier detection (OD) constitutes an important issue for many research areas namely data mining, medicines, and sensor networks. It is helpful mainly in identifying intrusion, fraud, errors, defects, noise and so on. In fact, outlier measurements are essential improvements to quality of information, as they are not conforming to expected normal behaviour. Due to the importance of sensed measurements is collected via wireless sensor networks, a novel OD process dubbed density-based spatial clustering of applications with noise (DBSCAN)-OD has been developed based on the algorithm DBSCAN, as a background for OD. With respect to the classic DBSCAN approach, two processes have been jointly combined, the first of computing parameters, while the second concerns class identification in spatial temporal databases. Through both of these modules, one is able to consider real-time application cases as centralised in the base station for the purpose of separating outliers from normal sensors. For the sake of evaluating the authors proposed solution, a diversity of synthetic databases has been applied as generated from real measurements of Intel Berkeley lab. The reached simulation findings indicate well that their devised method can prove to help effectively in detecting outliers with an accuracy rate of 99%.