Scientific contributions in the area of smart environments cover different tasks of ambient intelligence including action and activity recognition, anomaly detection, and automated enactment. Algorithms solving these tasks need to be validated against sensor logs of smart environments. In order to acquire these datasets, expensive facilities are needed, containing sensors, actuators and an acquisition infrastructure. Even though several freely accessible datasets are available, each of them features a very specific set of sensors, which can limit the introduction of novel approaches that could benefit of particular types of sensors and deployment layouts. Additionally, acquiring a dataset requires a considerable human effort for labeling purposes, thus further limiting the creation of new and general ones. In this paper, we propose a model-based simulator capable to generate synthetic datasets that emulate the characteristics of the vast majority of real datasets while granting trustworthy evaluation results. The datasets are generated using the eXtensible Event Stream – XES international standard commonly used for representing event logs. Finally, the datasets produced by the simulator are validated against two real scenario’s logs from the literature.

A model-based simulator for smart homes: Enabling reproducibility and standardization / Veneruso, Silvestro; Bertrand, Yannis; Leotta, Francesco; Serral, Estefanía; Mecella, Massimo. - In: JOURNAL OF AMBIENT INTELLIGENCE AND SMART ENVIRONMENTS. - ISSN 1876-1372. - 15:2(2023), pp. 143-163. [10.3233/AIS-220016]

A model-based simulator for smart homes: Enabling reproducibility and standardization

Veneruso, Silvestro
;
Leotta, Francesco;Mecella, Massimo
2023

Abstract

Scientific contributions in the area of smart environments cover different tasks of ambient intelligence including action and activity recognition, anomaly detection, and automated enactment. Algorithms solving these tasks need to be validated against sensor logs of smart environments. In order to acquire these datasets, expensive facilities are needed, containing sensors, actuators and an acquisition infrastructure. Even though several freely accessible datasets are available, each of them features a very specific set of sensors, which can limit the introduction of novel approaches that could benefit of particular types of sensors and deployment layouts. Additionally, acquiring a dataset requires a considerable human effort for labeling purposes, thus further limiting the creation of new and general ones. In this paper, we propose a model-based simulator capable to generate synthetic datasets that emulate the characteristics of the vast majority of real datasets while granting trustworthy evaluation results. The datasets are generated using the eXtensible Event Stream – XES international standard commonly used for representing event logs. Finally, the datasets produced by the simulator are validated against two real scenario’s logs from the literature.
2023
smart environments; simulation; data formats; reproducibility
01 Pubblicazione su rivista::01a Articolo in rivista
A model-based simulator for smart homes: Enabling reproducibility and standardization / Veneruso, Silvestro; Bertrand, Yannis; Leotta, Francesco; Serral, Estefanía; Mecella, Massimo. - In: JOURNAL OF AMBIENT INTELLIGENCE AND SMART ENVIRONMENTS. - ISSN 1876-1372. - 15:2(2023), pp. 143-163. [10.3233/AIS-220016]
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11573/1668696
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