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Open AccessArticle

An IOHMM-Based Framework to Investigate Drift in Effectiveness of IoT-Based Systems

1
CNRS, Laboratoire I3S, Université Côte d’Azur (UCA), UMR 7271, 06900 Sophia Antipolis, France
2
Telecom Physique, Université de Strasbourg, 67400 Illkirch-Graffenstaden, France
*
Author to whom correspondence should be addressed.
Sensors 2021, 21(2), 527; https://doi.org/10.3390/s21020527
Received: 17 December 2020 / Revised: 8 January 2021 / Accepted: 10 January 2021 / Published: 13 January 2021
(This article belongs to the Section Internet of Things)
IoT-based systems, when interacting with the physical environment through actuators, are complex systems difficult to model. Formal verification techniques carried out at design-time being often ineffective in this context, these systems have to be quantitatively evaluated for effectiveness at run-time, i.e., for the extent to which they behave as expected. This evaluation is achieved by confronting a model of the effects they should legitimately produce in different contexts to those observed in the field. However, this quantitative evaluation is not informative on the drifts in effectiveness, it does not help designers investigate their possible causes, increasing the time needed to resolve them. To address this problem, and assuming that models of legitimate behavior can be described by means of Input-Output Hidden Markov Models (IOHMMs), a novel generic unsupervised clustering-based IOHMM structure and parameters learning algorithm is developed. This algorithm is first used to learn a model of legitimate behavior. Then, a model of the observed behavior is learned from observations gathered in the field. A second algorithm builds a dissimilarity graph that makes clear structural and parametric differences between both models, thus providing guidance to designers to help them investigate possible causes of drift in effectiveness. The approach is validated on a real world dataset collected in a smart home. View Full-Text
Keywords: actuation; internet of things; ambient intelligence; cyber–physical systems; effectiveness; drift; input-output hidden markov models actuation; internet of things; ambient intelligence; cyber–physical systems; effectiveness; drift; input-output hidden markov models
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MDPI and ACS Style

Rocher, G.; Lavirotte, S.; Tigli, J.-Y.; Cotte, G.; Dechavanne, F. An IOHMM-Based Framework to Investigate Drift in Effectiveness of IoT-Based Systems. Sensors 2021, 21, 527. https://doi.org/10.3390/s21020527

AMA Style

Rocher G, Lavirotte S, Tigli J-Y, Cotte G, Dechavanne F. An IOHMM-Based Framework to Investigate Drift in Effectiveness of IoT-Based Systems. Sensors. 2021; 21(2):527. https://doi.org/10.3390/s21020527

Chicago/Turabian Style

Rocher, Gérald; Lavirotte, Stéphane; Tigli, Jean-Yves; Cotte, Guillaume; Dechavanne, Franck. 2021. "An IOHMM-Based Framework to Investigate Drift in Effectiveness of IoT-Based Systems" Sensors 21, no. 2: 527. https://doi.org/10.3390/s21020527

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