Fault detection in wireless sensor networks: A machine learning approach

Ehsan Ullah Warriach, Kenji Tei

研究成果: Paper査読

22 被引用数 (Scopus)

抄録

Wireless Sensor Network (WSN) deployment experiences show that collected data is prone to be faulty. Faults are due to internal and external influences, such as calibration, low battery, environmental interference and sensor aging. However, only few solutions exist to deal with faulty sensory data in WSN. We develop a statistical approach to detect and identify faults in a WSN. In particular, we focus on the identification and classification of data and system fault types as it is essential to perform accurate recovery actions. Our method uses Hidden Markov Models (HMMs) to capture the fault-free dynamics of an environment and dynamics of faulty data. It then performs a structural analysis of these HMMs to determine the type of data and system faults affecting sensor measurements. The approach is validated using real data obtained from over one month of samples from motes deployed in an actual living lab.

本文言語English
ページ758-765
ページ数8
DOI
出版ステータスPublished - 2013 12 1
外部発表はい
イベント2013 16th IEEE International Conference on Computational Science and Engineering, CSE 2013 - Sydney, NSW, Australia
継続期間: 2013 12 32013 12 5

Conference

Conference2013 16th IEEE International Conference on Computational Science and Engineering, CSE 2013
CountryAustralia
CitySydney, NSW
Period13/12/313/12/5

ASJC Scopus subject areas

  • Computer Science (miscellaneous)

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