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Open AccessProceedings

Automatic Detection of Erratic Sensor Observations in Ami Platforms: A Statistical Approach

by Diego Martín 1,*,‡, Borja Bordel 2,‡ and Ramón Alcarria 3,‡
1
ETSI Telecomunicación, Technical University of Madrid, Av. Complutense 30, 28040 Madrid, Spain
2
ETSI Sistemas Informáticos, Technical University of Madrid, Calle de Alan Turing s/n, 28031 Madrid, Spain
3
ETSI Topography, Geodetics and Cartography Technical, University of Madrid, Camino de la Arboleda s/n, 28031 Madrid, Spain
*
Author to whom correspondence should be addressed.
Presented at the 13th International Conference on Ubiquitous Computing and Ambient Intelligence UCAmI 2019, Toledo, Spain, 2–5 December 2019.
These authors contributed equally to this work.
Proceedings 2019, 31(1), 55; https://doi.org/10.3390/proceedings2019031055
Published: 20 November 2019
This paper addresses the problem of data aggregation platforms operating in heterogeneous Ambient Intelligence Environments. In these platforms, device interoperability is a challenge and erratic sensor observations are difficult to be detected. We propose ADES (Automatic Detection of Erratic Sensors), a statistical approach to detect erratic behavior in sensors and annotate those errors in a semantic platform. To do that, we propose three binary classification systems based on statistical tests for erratic observation detection, and we validate our approach by verifying whether ADES is able to classify sensors by its observations correctly. Results show that the first two classifiers (constant and random observations) had good accuracy rates, and they were able to classify most of the samples. In addition, all of the classifiers obtained a very low false positive rate.
Keywords: sensor observations; binary classifiers; AmI platform sensor observations; binary classifiers; AmI platform
MDPI and ACS Style

Martín, D.; Bordel, B.; Alcarria, R. Automatic Detection of Erratic Sensor Observations in Ami Platforms: A Statistical Approach . Proceedings 2019, 31, 55.

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