What is it about?
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 from smart environments. In order to acquire these datasets, expensive facilities are needed, containing sensors, actuators, and an acquisition infrastructure. In this publication, we propose a model-based simulator capable of generating synthetic datasets for the smart space community.
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Why is it important?
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 from particular types of sensors and deployment layouts. Additionally, acquiring a dataset requires considerable human effort for labeling purposes, thus further limiting the creation of new and general ones. Our simulator is capable of generating 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.
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This page is a summary of: A model-based simulator for smart homes: Enabling reproducibility and standardization, Journal of Ambient Intelligence and Smart Environments, June 2023, IOS Press, DOI: 10.3233/ais-220016.
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