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

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.