Scholarlink

This project investigates the building a mobile device using Arduino UNO that helps track the weather in real time [1]. It’s a simple four-wheeled unit loaded with sensors like DHT11 to check temperature, humidity, and air quality. As it moves around using 4-wheel drive with L298 Motor Driver, it sends the data to a cloud system. So, we use machine learning methods like Random Forest and LSTM to spot trends in the weather data and make short-term forecasts. The idea is to cut down on the need for someone to constantly watch over the system. It’s helpful in areas like farming, city planning, or hospitals, where knowing the weather quickly can really help. This makes monitoring easier and more practical, thanks to a smart mobile setup that runs on its ownss.

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Deployment of Mobile Weather Monitoring Unit for Prediction and Forecasting by Integrating IoT and Machine Learning

  • 会议论文

  • 发表日期：

    2026年

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  • Zatin Gupta,
  • Naman Kaushik,
  • Jayant Baisla,
  • Rashmirathi Upadhyay

摘要

This project investigates the building a mobile device using Arduino UNO that helps track the weather in real time [1]. It’s a simple four-wheeled unit loaded with sensors like DHT11 to check temperature, humidity, and air quality. As it moves around using 4-wheel drive with L298 Motor Driver, it sends the data to a cloud system. So, we use machine learning methods like Random Forest and LSTM to spot trends in the weather data and make short-term forecasts. The idea is to cut down on the need for someone to constantly watch over the system. It’s helpful in areas like farming, city planning, or hospitals, where knowing the weather quickly can really help. This makes monitoring easier and more practical, thanks to a smart mobile setup that runs on its ownss.