Outlier detection for wireless sensor networks using density‐based clustering approach
Open Access
- 1 August 2017
- journal article
- research article
- Published by Institution of Engineering and Technology (IET) in IET Wireless Sensor Systems
- Vol. 7 (4), 83-90
- https://doi.org/10.1049/iet-wss.2016.0044
Abstract
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%.Keywords
This publication has 21 references indexed in Scilit:
- D-FICCA: A density-based fuzzy imperialist competitive clustering algorithm for intrusion detection in wireless sensor networksMeasurement, 2014
- Cooperative game theoretic approach using fuzzy Q-learning for detecting and preventing intrusions in wireless sensor networksEngineering Applications of Artificial Intelligence, 2014
- An efficient routing algorithm to preserve $$k$$ k -coverage in wireless sensor networksThe Journal of Supercomputing, 2013
- Summarizing numeric spatial data streams by trend cluster discoveryData Mining and Knowledge Discovery, 2013
- Revised DBSCAN algorithm to cluster data with dense adjacent clustersChemometrics and Intelligent Laboratory Systems, 2013
- Statistics-based outlier detection for wireless sensor networksInternational Journal of Geographical Information Science, 2012
- Density-Based Clustering and Anomaly DetectionPublished by IntechOpen ,2012
- Outlier detection and countermeasure for hierarchical wireless sensor networksIET Information Security, 2010
- OPTICSACM SIGMOD Record, 1999
- Bootstrap Methods for Standard Errors, Confidence Intervals, and Other Measures of Statistical AccuracyStatistical Science, 1986