AOAME4FloWare: Ontology-based feature models for context-aware configurations in IoT applications

Feature models play a relevant role in capturing and consolidating knowledge within an IoT application (e.g., Smart Home). They facilitate the representation of the many possible systems and devices and their relationships that can be deployed to support an IoT application. Once this knowledge is crystallized, the feature model becomes a reusable resource for configuring specific context solutions, defined as scenarios (e.g., Smart Home solution for hospitals). However, deriving an appropriate configuration from the designed feature model requires deep knowledge of the targeted IoT application and its scenario requirements (e.g., different requirements appear in a smart home solution for a public hospital vs. a private home). To tackle this challenge, we propose AOAME4FloWare. Our ontology-based metamodeling approach empowers the integration of feature models with IoT-context ontologies, harnessing the latter’s power to facilitate the configuration of an IoT application based on specific development requirements. The Design Science Research methodology was followed, where IoT-context requirements were derived from a real-world IoT scenario. The proposed artifact has been evaluated on a smart hospital solution, showing that feature model configuration can be supported by exploiting the underlying domain ontologies.

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• https://ceur-ws.org/Vol-3804/paper8o.pdf

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@inproceedings{nokey,
title = {AOAME4FloWare: Ontology-based feature models for context-aware configurations in IoT applications},
author = {Arianna Fedeli, Martin Peraic, Emanuele Laurenzi and Andrea Polini},
url = {https://ceur-ws.org/Vol-3804/paper8o.pdf},
year  = {2024},
date = {2024-09-11},
booktitle = {BIR-WS 2024: BIR 2024 Workshops and Doctoral Consortium, 23rd International Conference on Perspectives in Business Informatics Research (BIR 2024)},
abstract = {Feature models play a relevant role in capturing and consolidating knowledge within an IoT application (e.g., Smart Home). They facilitate the representation of the many possible systems and devices and their relationships that can be deployed to support an IoT application. Once this knowledge is crystallized, the feature model becomes a reusable resource for configuring specific context solutions, defined as scenarios (e.g., Smart Home solution for hospitals). However, deriving an appropriate configuration from the designed feature model requires deep knowledge of the targeted IoT application and its scenario requirements (e.g., different requirements appear in a smart home solution for a public hospital vs. a private home). To tackle this challenge, we propose AOAME4FloWare. Our ontology-based metamodeling approach empowers the integration of feature models with IoT-context ontologies, harnessing the latter’s power to facilitate the configuration of an IoT application based on specific development requirements. The Design Science Research methodology was followed, where IoT-context requirements were derived from a real-world IoT scenario. The proposed artifact has been evaluated on a smart hospital solution, showing that feature model configuration can be supported by exploiting the underlying domain ontologies.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}