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Smoke detection
Environmental Monitoring
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About
This dataset captures environmental sensor data to monitor smoke detection and potential fire hazards. It includes a variety of physical and chemical parameters recorded over time, making it ideal for studying sensor readings, trend analysis, and anomaly detection.
Dataset Features:
- NO_ID A unique identifier for Smoke detection row.
- UTC: Unix Timestamp of the recording, representing the date and time of each observation.
- Temperature [°C]: Ambient temperature in degrees Celsius.
- Humidity [%]: Relative humidity percentage.
- TVOC [ppb]: Total Volatile Organic Compounds in parts per billion, indicating air quality.
- eCO2 [ppm]: Equivalent Carbon Dioxide in parts per million, representing air quality metrics.
- Raw H2: Raw sensor data for hydrogen levels.
- Raw Ethanol: Raw sensor data for ethanol levels.
- Pressure [hPa]: Atmospheric pressure in hectopascals.
- PM1.0: Particulate matter concentration for particles smaller than 1.0 µm in μg/m³.
- PM2.5: Particulate matter concentration for particles smaller than 2.5 µm in μg/m³.
- NC0.5: Number concentration for particles smaller than 0.5 µm in number/cm³.
- NC1.0: Number concentration for particles smaller than 1.0 µm in number/cm³.
- NC2.5: Number concentration for particles smaller than 2.5 µm in number/cm³.
- CNT: Cumulative count of the sensor's readings since initialisation.
- Fire Alarm: Binary indicator (0 or 1) denoting whether a fire alarm condition is triggered.
Usage:
The dataset is ideal for various applications, including:
- Environmental Monitoring: Analysing air quality and identifying hazardous conditions.
- Predictive Modeling: Developing models to predict fire alarms based on sensor readings.
- Time-Series Analysis: Investigating patterns and trends in environmental conditions.
- Sensor Calibration: Benchmarking sensor performance against real-world conditions. Smoke Detection: Analysing air quality to assess the safety of the environment for people.
Coverage:
This dataset provides comprehensive environmental metrics for indoor spaces, allowing for insights into air quality and safety conditions. The detailed sensor readings make it suitable for machine learning tasks and exploratory data analysis.
License:
CC0 (Public Domain)
Who Can Use It:
This dataset is intended for data scientists, researchers, IoT enthusiasts, and students interested in environmental analytics, safety monitoring, and IoT applications.
How to Use It:
- Train machine learning models to detect abnormal environmental conditions or predict fire alarms.
- Perform feature importance analysis to determine critical factors influencing air quality.
- Explore time-series data for trend detection and anomaly identification.
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