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  • Elhabbash2022middleware

    Rights statement: © ACM, 2022. This is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The definitive version was published in IoT '22: Proceedings of the 12th International Conference on the Internet of Things http://doi.acm.org/10.1145/3567445.3567451

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    Available under license: CC BY-NC: Creative Commons Attribution-NonCommercial 4.0 International License

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A Middleware for Automatic Composition and Mediation in IoT Systems

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSNConference contribution/Paperpeer-review

Published
Publication date7/11/2022
Host publicationIoT '22: Proceedings of the 12th International Conference on the Internet of Things
Place of PublicationNew York
PublisherACM
Pages127-134
Number of pages8
ISBN (electronic)9781450396653
<mark>Original language</mark>English

Publication series

NameProceedings of the 12th International Conference on the Internet of Things
PublisherACM

Abstract

This paper presents Hetero-Genius, a middleware architecture that enables construction and mediation in Internet of Things (IoT) systems. IoT systems are deployed across physical spaces such as urban parks, residential areas, and highways. The services provided by such IoT deployments are constrained to specific devices and deployment contexts. While existing interoperability solutions enable the “design time” development and deployment of IoT systems, it is often essential to dynamically compose systems that consist of other “small scale” IoT systems. To achieve this, post-deployment composition is needed, i.e., runtime composition of diverse IoT devices and capabilities. Hetero-Genius supports system and service discoverability, as well as automatic composability. We demonstrate this using a real-world Internet of Vehicles (IoV) scenario. Our experimental evaluation shows that developers can save up to 47% of their time when using Hetero-Genius, as well as improve code correctness by 55% on average.

Bibliographic note

© ACM, 2022. This is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The definitive version was published in IoT '22: Proceedings of the 12th International Conference on the Internet of Things http://doi.acm.org/10.1145/3567445.3567451