Fault classification and model learning from sensory Readings - Framework for fault tolerance in wireless sensor networks

Valentina Baljak, Kenji Tei, Shinichi Honiden

研究成果: Conference contribution

5 被引用数 (Scopus)

抄録

Primary task of wireless sensor networks is to deliver reliable and accurate information about the phenomena of interest. However, faults are a frequent occurrence and their accumulation affects the quality of service significantly. This leads to a shorter effective lifetime of the network. In this work, we propose a framework for the fault tolerance in sensory readings. The main concept is based on the observation of the pattern that faults leave in data behavior. Based on the duration, continuity and the impact, we propose a complete and consistent classification of faults as they can be observed in sensory readings independently of the underlying cause. Further, we propose that network learns a model of a fault for each faulty node from the past behavior. Each phase of the framework can be implemented with the use of different algorithms appropriate for the task. In this paper we present an instance that relies on neighborhood vote, time series analysis and statistical pattern recognition. Results so far confirm that the scheme works well for dense data-centric wireless sensor networks.

本文言語English
ホスト出版物のタイトルProceedings of the 2013 IEEE 8th International Conference on Intelligent Sensors, Sensor Networks and Information Processing
ホスト出版物のサブタイトルSensing the Future, ISSNIP 2013
ページ408-413
ページ数6
DOI
出版ステータスPublished - 2013 8 9
外部発表はい
イベント2013 IEEE 8th International Conference on Intelligent Sensors, Sensor Networks and Information Processing: Sensing the Future, ISSNIP 2013 - Melbourne, VIC, Australia
継続期間: 2013 4 22013 4 5

出版物シリーズ

名前Proceedings of the 2013 IEEE 8th International Conference on Intelligent Sensors, Sensor Networks and Information Processing: Sensing the Future, ISSNIP 2013
1

Other

Other2013 IEEE 8th International Conference on Intelligent Sensors, Sensor Networks and Information Processing: Sensing the Future, ISSNIP 2013
CountryAustralia
CityMelbourne, VIC
Period13/4/213/4/5

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Networks and Communications
  • Information Systems

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