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SmartUV - UV rays Monitoring & Forecasting system

Subject: Advanced Internet of Things Technology
The duration of the project is from March 2024 to May 2024

Members

  1. Nguyen Cao Thi
  2. Nguyen Tra Bao Ngan

Abstract

Implemented an UV monitoring and forecasting system using ML8511 UV sensor, DHT22 sensor, and ESP32 microcontroller. The system collects real-time UV index, temperature, and humidity data from the environment. The collected data is then processed and used to predict the UV index for the next day. The system also provides a mobile app for displaying the current UV index, temperature, humidity, and predicted UV index for the next day.

System achitecture

image

Things layer circuit diagram

image

Technologies

  1. Devices: Raspberry Pi 4, DHT22, ML8511, ESP32
  2. Services: Azure, MongoDB, Docker, FastAPI, Deeplearning

References

[1] Raspberry Việt Nam, 08/01/2014. “Raspberry là gì? Giới thiệu về Raspberry Pi”.
[2] akshaybotre203, 02/11/2023. “Architecture of Raspberry Pi”.
[3] Nate. “ML8511 UV Sensor Hookup Guide”.
[4] Eduardo Pecina, 09/07/2019. “OS Options for Raspberry Pi 4”.
[5] Lê Trung Quân, Huỳnh Văn Đặng, Nguyễn Khánh Thuật, 13/07/2022. “Giáo trình Công nghệ Internet of Things và ứng dụng”.
[6] Trang Vũ, 29-09-2023, “MongoDB là gì? Hiểu về cơ sở dữ liệu phi quan hệ MongoDB”.
[7] Hochreiter, S.; and Schmidhuber, J. 1997. “Long Short-Term Memory”. Neural Computation, 9(8): 1735–1780
[8] Cho, K., van Merriënboer, B., and Bengio, Y., “On Using Very Large Target Vocabulary for Neural Machine Translation” arXiv preprint arXiv:1412.2007, 2014.
[9] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł. and Polosukhin, I., “Attention is all you need - Advances in neural information processing systems”, vol. 30, 2017.
[10] Andrew G Howard, Menglong Zhu, Bo Chen, Dmitry Kalenichenko, Weijun Wang, Tobias Weyand, Marco An dreetto, and Hartwig Adam. “Mobilenets: Efficient convolutional neural networks for mobile vision applications”. arXiv preprint arXiv:1704.04861, 2017.
[11] Mark Sandler, Andrew Howard, Menglong Zhu, Andrey Zh moginov, and Liang Chieh Chen. “Mobilenetv2: Inverted residuals and linear bottlenecks”. 2018.
[12] Andrew Howard, Mark Sandler, Grace Chu, Liang-Chieh Chen, Bo Chen, Mingxing Tan, Weijun Wang, Yukun Zhu, Ruoming Pang, Vijay Vasudevan, Quoc V.Le, and Hartwig Adam. “Searching for mobilenetv3”. October 2019.
[13] Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., Uszkoreit, J., & Houlsby, N. (2021). “An image is worth 16x16 words: Transformers for image recognition at scale”. 2021.
[14] Yinpeng Chen, Xiyang Dai, Dongdong Chen, Mengchen Liu, Xiaoyi Dong, Lu Yuan, and Zicheng Liu, “Mobile-Former: Bridging MobileNet and Transformer” 2022, pp. 5270-5280.

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