An Energy-Efficient Outlier Detection Based on Data Clustering in WSNs

Hongyeon Kim and Jun-Ki Min

International Journal of Distributed Sensor Networks, 2014, vol. 10, issue 4, 619313

Abstract: Sensor nodes in wireless sensor networks are prone to malfunction because they are exposed to the nearby environment directly. Consequently, wrong sensor readings occurred from sensor nodes and these readings are called an outlier. Commonly, since an outlier deviates from normal sensor readings and it can bring about some problems, various techniques to detect the outliers have been proposed. In this paper, we propose an efficient outlier detection technique based on data clustering. In order to decide the width of the cluster that consists of the sensor readings, we applied the Pigeonhole Principle and then detected the outliers based on clusters. In experiments, we demonstrate the efficiency of our proposed technique compared to other outlier detection techniques.

Date: 2014
References: Add references at CitEc
Citations:

Downloads: (external link)
https://journals.sagepub.com/doi/10.1155/2014/619313 (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:sae:intdis:v:10:y:2014:i:4:p:619313

DOI: 10.1155/2014/619313

Access Statistics for this article

More articles in International Journal of Distributed Sensor Networks
Bibliographic data for series maintained by SAGE Publications ().