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This survey of literature examines the recent developments in ultra-lightweight and energy-efficient deep learning models which are specifically created to fit devices with limited resources and that are deployed in the IoT. The survey examines the ways of reducing model size and optimization of computation and balancing security and energy efficiency. The research on lightweight encryption and object recognition proves the application of these models in low-power settings in practice. The questionnaire will serve to present an overall picture of the methods and algorithms that allow the effective application of deep learning to the limited Internet of Things devices.
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Copyright © 2025, This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC-BY-NY-SA). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
*Corresponding Author: D Shaik Abdulla, udaykrishna696@gmail.com
Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.
Conflict of interest: The author declares that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
Publisher’s note: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
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