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Sensor Web Project

This project is a graphical user interface (GUI) application built using Python and customtkinter. It integrates APIs from NASA and OpenWeather to create a network of sensors capable of retrieving satellite images, weather data, and other environmental observations based on user-provided coordinates. The project also includes an anomaly detection feature using IsolationForest to analyze sensor data.

Table of Contents

Introduction

The Sensor Web Project is designed to simulate a network of sensors capable of real-time data acquisition and analysis. It serves as a prototype for autonomous observation systems, demonstrating how satellite imagery and weather data can be integrated for environmental monitoring.

Sensor Web Project Screenshot

Objectives

  • Build a software system to integrate satellite and weather data from external APIs.
  • Visualize sensor data and detect anomalies using machine learning techniques.
  • Provide an intuitive graphical user interface for interaction.

System Architecture

The system includes:

  1. Data Acquisition: Uses NASA Earth Assets API for satellite images and OpenWeather API for weather data.
  2. Data Processing: Generates synthetic sensor readings and detects anomalies using the IsolationForest algorithm.
  3. Visualization: Displays sensor data and satellite images using matplotlib and PIL.

Features

  • Sensor Data Simulation: Generates synthetic temperature data using numpy.
  • Anomaly Detection: Detects anomalies using IsolationForest from scikit-learn.
  • Satellite Imagery: Retrieves images from the NASA Earth Assets API based on user coordinates.
  • Weather Data: Fetches current weather conditions using OpenWeather API.
  • Interactive GUI: Allows users to input coordinates and view real-time results.

Technologies Used

  • Python
  • customtkinter for the GUI
  • requests for API calls
  • scikit-learn for anomaly detection
  • matplotlib for plotting graphs
  • PIL (Pillow) for image handling
  • NASA Earth Assets API
  • OpenWeather API

Installation

  1. Clone the repository:

    git clone https://github.com/yourusername/sensor-web-project.git
cd sensor-web-project

usage

  • Enter Latitude and Longitude: The application fetches satellite images and weather data for the specified coordinates.
  • Generate Sensor Data: Simulates a set of sensor readings.
  • Anomaly Detection: Analyzes the generated sensor data and identifies anomalies.

Configuration

  • Ensure your environment is properly set up with the required API keys.
  • Adjust parameters like sensor data count or plotting dimensions if needed.

Project Structure

  • sensor-web-project
  • main.py Main entry point of the application.
  • requirements.txt List of dependencies.
  • README.md Project documentation.

Results

  • The project demonstrates successful integration of real-world data from external APIs with simulated sensor data. It provides:

  • Real-time visualization of satellite images based on user inputs.

  • Accurate weather data retrieval for any location worldwide.

  • Detection of outliers in sensor data, highlighting potential anomalies.

Limitations

  • Requires API keys with potential usage limits.
  • Relies on internet connectivity for data fetching.
  • Synthetic sensor data may not fully represent real-world conditions.

Future Enhancements

  • Integrate more advanced machine learning models for anomaly detection.
  • Expand the sensor network to include additional environmental data like air quality.
  • Implement a database for historical data storage. -Develop a web-based version for broader accessibility.

Contributing

Feel free to submit issues, fork the repository, and create pull requests.

License

This project is licensed under the MIT License.

References