Predicting Weather Conditions by the Internet of Things Platform
- DOI
- 10.2991/978-94-6463-092-3_15How to use a DOI?
- Keywords
- Weather station; Esp32; IoT. Firebase
- Abstract
Weather stations are built to collect quantitative data on the weather conditions of a place. Monitoring the weather conditions in the current environment is considered to be very important because the erratic weather changes every day. This study tried to create a weather station that can be accessed through the website using the IoT platform. Users can know the weather changes in an area without the need to come to the area. The design of this weather station uses the main components of the ESP32 microcontroller because this microcontroller is already integrated with Bluetooth and wifi. The design results of this tool can measure data weather consisting of temperature parameters, humidity, air pressure, rainfall, wind speed, wind direction as well as day, night, and cloudy conditions. The results of measuring the parameters of weather data are stored on the cloud firebase database server and can be accessed via an android mobile phone.
- Copyright
- © 2023 The Author(s)
- Open Access
- Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.
Cite this article
TY - CONF AU - Wiwid Suryono AU - Ariyono Setiawan AU - Anton Budiarto AU - Yuyun Suprapto AU - S. Sri Rahayu AU - Ahmad Musadek PY - 2023 DA - 2023/02/21 TI - Predicting Weather Conditions by the Internet of Things Platform BT - Proceedings of the International Conference on Advance Transportation, Engineering, and Applied Science (ICATEAS 2022) PB - Atlantis Press SP - 168 EP - 178 SN - 2352-5401 UR - https://doi.org/10.2991/978-94-6463-092-3_15 DO - 10.2991/978-94-6463-092-3_15 ID - Suryono2023 ER -