Deep Learning Research on Smart Home Compatibility

Bowen Zhang

doi:10.54254/2755-2721/96/20241450

Research Article

Open access

Published on 26 November 2024

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Zhang,B. (2024). Deep Learning Research on Smart Home Compatibility. Applied and Computational Engineering,96,1-6.

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Deep Learning Research on Smart Home Compatibility

Bowen Zhang *^,1,

¹ College of Electronic and Information Engineering, Southwest University, ChongQing, China, 400715

* Author to whom correspondence should be addressed.

https://doi.org/10.54254/2755-2721/96/20241450

Abstract

In recent years, with the rapid development of technology, the concept of “Smart Home” has gradually emerged into people's view., Because of its remarkable convenience, it has won the favor of many consumers in the present era. However, as the utilization of smart home devices becomes more widespread, several challenges have emerged that need to be addressed for future development. One such challenge is the compatibility problem, which has now transformed into a significant barrier hindering the development of Smart Home. Therefore, in order to bring back a truly intelligent smart home environment, it is of utmost urgency to address the compatibility problem. In this paper, the author mainly focuses on this issue. By leveraging the knowledge of IoT and transport protocols, and using Python as the research and test platform, the researcher strives to either work out or alleviate the compatibility problem within the Smart Home domain.

Keywords

Compatibility, Smart Home, IoT, Transport Protocols.

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References

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Cite this article

Zhang,B. (2024). Deep Learning Research on Smart Home Compatibility. Applied and Computational Engineering,96,1-6.

Data availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

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About volume

Volume title: Proceedings of the 2nd International Conference on Machine Learning and Automation

Conference website: https://2024.confmla.org/

ISBN：978-1-83558-671-6(Print) / 978-1-83558-672-3(Online)

Conference date: 21 November 2024

Editor：Mustafa ISTANBULLU

Series: Applied and Computational Engineering

Volume number: Vol.96

ISSN：2755-2721(Print) / 2755-273X(Online)

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