Notice of Violation of IEEE Publication Principles: A hybrid fault detection approach for context-aware wireless sensor networks

Notice of Violation of IEEE Publication Principles

"A Hybrid Fault Detection Approach for Context-aware Wireless Sensor Networks,"
by E.U. Warriach, Tuan Anh Nguyen, M. Aiello, and Tei Kenji,

in the Proceedings of the IEEE 9th International Conference on Mobile Adhoc and Sensor Systems (MASS), October 2012, pp.281-289

After the authors contacted the conference organizers for withdrawing this paper due to plagiarism performed by the lead author, as confirmed by careful and considered review of the content and authorship by a duly constituted expert committee, this paper has been confirmed being in violation of IEEE’s Publication Principles.

This paper contains significant portions of original text from the paper cited below. The original text was copied without attribution (including appropriate references to the original author(s) and/or paper title) and without permission. The lead author, E.U. Warriach, was responsible for the misconduct, and the coauthors were unaware of the copied material at time of submission

. "On the Prevalence of Sensor Faults in Real-World Deployments,"
by A. Sharma, L. Golubchik, and R. Govindan,
in the Proceedings of the 4th Annual IEEE Communications Society Conference on Sensor, Mesh and Ad Hoc Communications and Networks (SECON), June 2007, pp.213-222


Wireless Sensor Network (WSN) deployment experiences show that data collected is prone to be imprecise and faulty due to internal and external influences, such as battery drain, environmental interference, sensor aging. An early detection of such faults is necessary for the effective operation of the sensor network. We focus on identifying data fault types and their causes. In particular, we propose a hybrid approach to the detection of faults based on three qualitatively different classes of fault detection methods. Rule-based methods leverage domain and expert knowledge to develop heuristic rules for identifying and classifying faults. Estimation methods predict normal sensor behavior by leveraging sensor spatial and temporal correlations, identifying erroneous sensor readings as faults. Finally, learning-based methods are inferred a model for the faulty sensor readings using training data and statistically detect and identify classes of faults. We illustrate the performance of a hybrid approach on data coming from two actual sensor deployments.