6,400 mono audio clips in seven event classes for edge acoustic-event research, with a three-field manifest and documented reproducibility limitations.
15-minute ESP32 environmental and solar-voltage data: 84 training days and 31 validation days in Zapopan, plus a separate Ulm trial; forecasts are one step ahead.
IoT environmental sensor dataset tracking water quality and pollution risk indicators for Gaoyou Lake, China. Includes pH, dissolved oxygen, turbidity, temperature, and conductivity readings. CSV format via Kaggle. Used for aquatic pollution risk classification and IoT water monitoring research.
Longest freely available on-field IoT air quality sensor deployment: 9,358 hourly records from 5 metal oxide gas sensors in an Italian city. CSV/XLSX format. Used for gas sensor regression, drift correction, and pollution forecasting research.
Smart-home energy dataset with detailed electrical, environmental, and operational streams from 3 real homes plus minute-level electricity data from 400+ homes. Open portal export format. Used for sustainable home and demand analysis.
Published in November 2025, this dataset provides a multimodal framework integrating satellite imagery and IoT sensor data for environmental monitoring and disaster management. It is designed to support the development of machine learning models that synchronize remote sensing with ground-based IoT observations for real-time risk assessment.
AIR4LIFE is a high-resolution air quality monitoring dataset published in late 2025, featuring measurements from a dual-node IoT setup. It tracks pollutants and ambient conditions to evaluate the trade-offs between energy-aware duty cycling and data completeness in environmental sensing networks.
UrbanAirNet provides a comprehensive collection of urban air quality and weather parameters measured via IoT sensor networks. It includes pollutants like PM2.5, NO2, and O3 alongside meteorological variables.
A real-world IoT dataset from a multi-purpose university building at University of Sharjah, capturing appliance-level energy consumption, temperature, humidity, and occupancy, along with 2D Markov Transition Field (MTF) image representations for deep learning, published in Data in Brief.
Curated collection of 10,000+ realistic IoT sensor readings from Bangladesh representing diverse environmental conditions including temperature, humidity, soil moisture, rainfall, and air quality with timestamps and location tags for smart agriculture and climate research.