https://github.com/samsam19191/airwatcher
AirWatcher is a software application developed for a government agency to monitor and analyze air quality data across a large territory.
https://github.com/samsam19191/airwatcher
cpp
Last synced: about 1 year ago
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AirWatcher is a software application developed for a government agency to monitor and analyze air quality data across a large territory.
- Host: GitHub
- URL: https://github.com/samsam19191/airwatcher
- Owner: Samsam19191
- Created: 2024-10-16T12:12:13.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2024-10-21T13:09:37.000Z (over 1 year ago)
- Last Synced: 2025-05-24T19:42:41.082Z (about 1 year ago)
- Topics: cpp
- Language: C++
- Homepage:
- Size: 2.62 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
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Metadata Files:
- Readme: README.md
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README
# AirWatcher: Air Quality Monitoring Application
## Overview
AirWatcher is a software application developed for a government agency to monitor and analyze air quality data across a large territory. This project was completed as part of a Software Engineering Lab series at INSA Lyon.
The application processes data collected from sensors distributed across the region to help identify malfunctioning sensors, calculate air quality statistics, and track the impact of industrial air cleaners. It also includes functionality for ranking sensors based on air quality similarity and detecting unreliable data from privately owned sensors.
## Key Features
- **Sensor Health Monitoring:** Analyze sensor data to detect malfunctions and reliability issues.
- **Air Quality Statistics:** Calculate mean air quality in a user-specified area or time period.
- **Sensor Similarity Ranking:** Rank sensors based on air quality similarity during a specified time period.
- **Impact Analysis of Air Cleaners:** Monitor the influence of industrial air cleaners on air quality.
- **Geographic Estimation:** Provide air quality estimates for precise locations, even without a sensor present.
- **Private Sensor Participation:** Manage sensors installed by private individuals, rewarding data contribution while identifying potential malicious activity.
## Algorithms
The application includes efficient algorithms for:
- Sensor malfunction detection
- Air quality aggregation and statistics
- Sensor similarity computation
- Classification of unreliable sensors