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Evaluation of the Energy Viability of Smart IoT Sensors Using TinyML for Computer Vision Applications: A Case Study

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Internet of Things. Advances in Information and Communication Technology (IFIPIoT 2023)

Part of the book series: IFIP Advances in Information and Communication Technology ((IFIPAICT,volume 683))

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Abstract

TinyML technology, situated at the intersection of Machine Learning, Embedded Systems, and the Internet of Things (IoT), presents a promising solution for a wide range of IoT domains. However, achieving successful deployment of this technology on embedded devices necessitates optimizing energy efficiency. To validate the feasibility of TinyML on embedded devices, extensive field research and real-world experiments were conducted. Specifically, a TinyML computer vision model for people detection was implemented on an embedded system installed in a turnstile at a Federal Institute. The device accurately counts people, monitors battery levels, and transmits real-time data to the cloud. Encouraging results were obtained from the prototype, and experiments were performed using a lithium battery configuration with three batteries in series. Hourly voltage consumption analysis was conducted, and the findings were illustrated through graphical representations. The camera sensor prototype exhibited a consumption rate of 1.25 V per hour, whereas the prototype without the camera sensor displayed a more sustainable consumption rate of 0.93 V per hour. This field research contributes to advancing TinyML applications and enriching studies regarding its integration with IoT and computer vision.

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Notes

  1. 1.

    https://www.tinyml.org/home/.

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Acknowledgments

I would like to express my sincere gratitude for the incredible teaching I received during my academic journey. I know that everything I have learned would not have been possible without the commitment, dedication, and passion that you put into your classes every day. The knowledge I have gained thanks to you is something I will carry with me forever. Additionally, I would like to thank FAPES for granting me the scholarship that made it possible for me to pursue my master’s degree at IFES. Without this opportunity, I would not have had access to all the learning and academic community I have encountered. Finally, I would like to thank IFIP-IOT for giving me the chance to publish my research work. It is an honor to be able to contribute to the academic community and I hope that my work can be useful to those who read it.

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Authors and Affiliations

  1. Instituto Federal do Espírito Santo Campus Serra, Serra, ES, 29166-630, Brazil

    Adriel Monti De Nardi & Maxwell Eduardo Monteiro

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  1. Adriel Monti De Nardi
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  2. Maxwell Eduardo Monteiro
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Correspondence to Maxwell Eduardo Monteiro .

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Editors and Affiliations

  1. Khalifa University, Abu Dhabi, United Arab Emirates

    Deepak Puthal

  2. University of North Texas, Denton, TX, USA

    Saraju Mohanty

  3. University of Missouri at Kansas City, Kansas City, MO, USA

    Baek-Young Choi

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De Nardi, A.M., Monteiro, M.E. (2024). Evaluation of the Energy Viability of Smart IoT Sensors Using TinyML for Computer Vision Applications: A Case Study. In: Puthal, D., Mohanty, S., Choi, BY. (eds) Internet of Things. Advances in Information and Communication Technology. IFIPIoT 2023. IFIP Advances in Information and Communication Technology, vol 683. Springer, Cham. https://doi.org/10.1007/978-3-031-45878-1_2

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  • DOI: https://doi.org/10.1007/978-3-031-45878-1_2

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