Benchmark Datasets for Fault Detection and Classification in Sensor Data

Bas de Bruijn, Tuan Anh Nguyen, Doina Bucur, Kenji Tei

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

10 Citations (Scopus)


Data measured and collected from embedded sensors often contains faults, i.e., data points which are not an accurate representation of the physical phenomenon monitored by the sensor. These data faults may be caused by deployment conditions outside the operational bounds for the node, and short- or long-term hardware, software, or communication problems. On the other hand, the applications will expect accurate sensor data, and recent literature proposes algorithmic solutions for the fault detection and classification in sensor data. In order to evaluate the performance of such solutions, however, the field lacks a set of \emph{benchmark sensor datasets}. A benchmark dataset ideally satisfies the following criteria: (a) it is based on real-world raw sensor data from various types of sensor deployments; (b) it contains (natural or artificially injected) faulty data points reflecting various problems in the deployment, including missing data points; and (c) all data points are annotated
Original languageEnglish
Title of host publicationProceedings of the 5th International Confererence on Sensor Networks
ISBN (Print)978-989-758-169-4
Publication statusPublished - 2016
Externally publishedYes
Event5th International Conference on Sensor Networks - Rome, Italy
Duration: 19 Feb 201621 Feb 2016
Conference number: 5


Conference5th International Conference on Sensor Networks
Abbreviated titleSENSORNETS 2016


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