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.
This Industrial IoT dataset provides synthetic yet realistic sensor data simulating equipment operation under normal and various failure conditions. Designed for predictive maintenance and machine learning, it includes sensor specifications, operational thresholds, and failure labels, allowing researchers to develop anomaly detection models without the constraints of sensitive real-world industrial data.
A merged and optimized dataset combining N-BaIoT (IoT-specific traffic) and UNSW-NB15 (general network threats) with feature engineering, dimensionality reduction, and benchmarked ML models (Decision Tree, SVM, Random Forest, Neural Network) for IoT anomaly detection, published in Scientific Reports.
Multi-resolution smart building energy dataset for forecasting competition with three versions: 1-year at 5-min intervals (v1.0), 40-day at 5-min (v2.0), and 1-day hourly (v3.x), designed to benchmark state-of-the-art energy prediction techniques.
Cold storage monitoring dataset from IoT-enabled system designed for smallholder farmers in Uganda, featuring temperature, humidity, door events, and power status for training predictive models to classify environmental conditions and assess post-harvest food spoilage risk.
Industrial sensor dataset for predictive maintenance research featuring real-time sensor data and historical equipment records from smart manufacturing systems, enabling machine learning models (decision trees, neural networks) to forecast equipment failures and optimize maintenance scheduling.
Curated machine-learning-ready dataset from NASA’s Solar Dynamics Observatory (SDO) mission integrating multiple solar instruments for flare prediction and solar activity modeling.
Specialized dataset containing features influencing vehicle collisions in Internet of Vehicles (IoV) networks. Includes V2V communication data, sensor readings, traffic conditions, and collision indicators for developing intelligent collision detection and prevention systems.
Multivariate dataset exploring the integration of IoT sensor data with AI algorithms across multiple domains. Features diverse sensor types, environmental parameters, and AI model performance metrics for hybrid intelligent systems research.
Dataset for evaluating federated learning approaches to IoT intrusion detection published in Nature Scientific Reports January 2026. Features distributed network traffic from multiple IoT deployments with privacy constraints and decentralized learning evaluation metrics.
Comprehensive large-scale IoT intrusion detection dataset from Canadian Institute for Cybersecurity with 33 attack types across 105 real IoT devices. Includes 8.94 GB of network traffic data covering DDoS, DoS, Mirai, MITM, and reconnaissance attacks.
Enhanced smart home energy consumption dataset with minute-resolution monitoring of 13+ appliances and regional weather data. Includes traditional appliances plus new IoT devices like car chargers, water heaters, pool pumps, and outdoor lighting.