Sensor-Based Environmental Monitoring Dataset: Temperature & Humidity

Dataset Description: Environmental Sensor Readings from Mars Rover Prototype

Research Hypothesis
A scaled-down Mars Rover prototype can effectively collect temperature and humidity data, demonstrating how real-time environmental monitoring can be used for autonomous navigation, climate analysis, and anomaly detection.

By analyzing the collected data, we aim to identify trends, evaluate sensor accuracy, and explore potential improvements in robotic exploration. This includes assessing response time, consistency, and anomalies caused by external factors like human interference or sudden environmental changes.

What the Data Shows
This dataset contains timestamped temperature and humidity readings collected at regular time intervals by the rover’s onboard DHT22 sensor. The data highlights:
- Gradual fluctuations in environmental conditions.
- Notable temperature spikes (~10°C) introduced using a lighter to test sensor response.
- Stable humidity levels with minor deviations due to air circulation or sensor drift.

Notable Findings
- Controlled Temperature Spikes: Short bursts of heat resulted in clear temperature increases (~10°C), demonstrating the sensor's ability to detect and log transient changes.
- Humidity Stability: Humidity levels remained within a narrow range, confirming minimal impact from applied temperature fluctuations.
- Gradual Environmental Variations: Small temperature and humidity shifts were observed, likely due to ambient conditions and ventilation effects.

How the Data Was Gathered
- Sensor Used: DHT22 (for temperature & humidity).
- Data Collection Frequency: Logged every few seconds.
- Controlled Testing: Heat spikes added using a lighter to simulate external interference.
- Data Transmission: Logged in real-time via wireless communication to a laptop.

How to Interpret and Use the Data
- Identify Trends: Observe temperature and humidity variations over time.
- Detect Anomalies: Locate sharp temperature spikes (~10°C increases) caused by external heating.
- Compare Sensor Performance: Evaluate how quickly temperature normalizes after a spike.
- Develop Predictive Models: Train machine learning models to predict environmental changes.

Potential Applications
- Autonomous Environment Monitoring: Detecting and responding to environmental anomalies.
- Sensor Calibration & Validation: Testing DHT22 sensor accuracy under different conditions.
- Climate Simulation & Research: Indoor climate modeling & environmental trend analysis.
- Robotics & AI: Training AI for automated responses to climate fluctuations.