Longitudinal Indoor Air Quality Dataset Collected Using a Low-Cost Multi-Sensor IoT Monitoring Platform
This repository contains a longitudinal indoor air quality (IAQ) dataset collected with a Raspberry Pi 5–based multi-sensor IoT monitoring platform deployed in an indoor laboratory. The monitoring campaign spans approximately 15.6 days of continuous post-initialization operation and includes synchronized measurements from multiple low-cost environmental sensors. The dataset contains timestamped observations of particulate matter (PM2.5), total volatile organic compounds (TVOC), equivalent carbon dioxide (eCO₂), temperature, relative humidity, atmospheric pressure, gas resistance, VOC Index, and MQ135 sensor measurements. Data were recorded at regular sampling intervals to capture both short-term fluctuations and long-term temporal variations in indoor air quality. In addition to the complete monitoring dataset, this repository includes curated subsets for various research applications, such as signal processing, machine learning, adaptive control, and indoor air quality assessment. These subsets are provided to facilitate reproducibility while preserving the complete master dataset for future studies. The dataset is designed to facilitate research in the following areas: indoor environmental monitoring, low-cost sensing, Internet of Things (IoT) applications, smart manufacturing, data-driven modeling, and environmental analytics. It may also function as a benchmark dataset for the development and evaluation of algorithms for intelligent decision-support systems, forecasting, classification, anomaly detection, and sensor signal processing. The repository comprises the entire dataset, research subsets, a variable dictionary, and documentation that delineates the dataset structure and measurement variables. Future dataset versions will incorporate additional documentation, source code, and links to relevant publications.