Agricultural IoT network intrusion dataset with 1.31 million labeled flow records (532 MB) emulating a real AG-IoT farm environment. Covers crop health, weather, and soil condition data with network attack scenarios. CSV via Zenodo. Used for smart farming security research.
Large-scale quality-assessed ICU PPG benchmark derived from MIMIC-III, with ECG, ABP, and respiration signals in 30-second WFDB segments. Multi-task format supporting cardiovascular and respiratory signal analysis for wearable algorithm development.
MIMIC-III is a globally recognized database featuring de-identified health data from 40,000+ ICU patients, integrating vital signs, lab results, and IoT device outputs for research.
MuST-C is a multi-sensor agricultural dataset for in-field phenotyping, covering six crop species with RGB, LiDAR, and multispectral data to automate large-scale growth monitoring.
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.
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.
AI-IoT-based Smart Drip Irrigation System (SDIS) dataset specifically designed for rice plants considering local agronomic characteristics, featuring soil moisture, weather data, and irrigation control decisions for precision agriculture.
Dataset documenting IoT sensor deployments in horticulture operations including greenhouse monitoring, fruit/vegetable cultivation parameters, and automated control systems for temperature, humidity, light, and nutrient delivery.
Comprehensive multi-sensor dataset with 9 parameters including environmental (temperature, humidity, light) and soil measurements (moisture, temperature, pH) plus solar battery voltage and water TDS, collected via Arduino-ESP8266 system with cloud integration.
67,830 record sets of multi-channel physiologic waveforms (ECG, ABP, respiration, PPG) and vital sign time series from approximately 30,000 ICU patients, supporting clinical research and ML model development for critical care.
Real-time sensor data for automated underground drip irrigation of tomato crops, including soil moisture, NPK (N, P, K), temperature, humidity, pressure, wind speed, and solar radiation collected via Edge IoT.[page:2][web:238]