RT Journal Article
A1 Aymen Abid
AD CES-Lab, ENIS, University of Sfax, Sfax, Tunisia
A1 Abdennaceur Kachouri
AD LETI-Lab, ENIS, University of Sfax, Sfax, Tunisia
A1 Adel Mahfoudhi
AD CCIT, University of Taif, Taif, Saudi Arabia

PB iet
T1 Outlier detection for wireless sensor networks using density-based clustering approach
JN IET Wireless Sensor Systems
VO 7
IS 4
SP 83
OP 90
AB 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%.
K1 density-based clustering
K1 spatial temporal databases
K1 DBSCAN algorithm
K1 wireless sensor networks
K1 class identification
K1 synthetic databases
K1 outlier measurements
K1 real-time application
K1 density-based spatial clustering of applications with noise
K1 outlier detection
K1 DBSCAN-OD
K1 information quality improvements
K1 Intel Berkeley lab
DO https://doi.org/10.1049/iet-wss.2016.0044
UL https://digital-library.theiet.org/;jsessionid=8qkm6k9ke45ht.x-iet-live-01content/journals/10.1049/iet-wss.2016.0044
LA English
SN 2043-6386
YR 2017
OL EN