The system integrates multi-sensordata acquisition with edge-based intelligence to overcome thelimitations of centralized and region-level weather forecasting services.
The proposed system employs locally deployed weather sensors interfaced with an Arduino Due microcontroller to continuously monitor environmental parameters, including temperature, humidity, atmospheric pressure, precipitation, windspeed, ultraviolet (UV) index, air quality, and overall weather conditions.
These parameters are selected to capture short-term atmospheric variations at the which it be as device presented at any location.
Before transferring the sensor data to the Raspberry pi 5, we need to do remote access and monitoring controlling to ensure reliable system operation. Where tools to use Real VNC Viewer, PuTTY, Ethernet cable, and a remote personal computer are used to set up sensor interfaces, check data transmission through the connection between arduino to pi, and monitor system health.
This process helps make sure data is collected accurately and communication between systems. When verification is been completed, live sensor data is sent from the Arduino Due to the Raspberry Pi 5 using a USB serial connection. The Raspberry Pi acts as the edge computing platform. After then, Data receiving, logging, and pre-processing are performed using Python-based libraries such as PySerial and Pandas by installing it by selecting the exetension version of python which are need to be satisfied.
The acquired sensor data is stored locally in CSV format for time-series analysis. Data pre-processing includes standard scaler and label encoding to prepare sensor observations for deep learning models. Required machine learning and deep learning libraries, along with visualization frameworks, are installed on the edge device to support real-time execution and monitoring.
An RNN, GRU and long short-term memory (LSTM) model is be applied to the pre-processing the data. It forecasts weather parameters for the next 10 days using both historical in past and live sensor inputs an present. The forecasting process is executed entirely at the edge.