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.
Reproducible large-scale IoT network dataset from 78 emulated devices using MQTT, CoAP, and RTSP protocols. Includes benign and malicious traffic with DoS, brute force, scanning, and C&C attacks in PCAP and CSV formats.
AirIoT is an open-access dataset for IoT-based air pollution monitoring, featuring real-time sensor readings for PM2.5, PM10, temperature, and humidity from urban deployments.
A comprehensive, global dataset of hydropower plants, tracking their status, location, and capacity to support analysis of renewable energy infrastructure.
Public (anonymized) predictive maintenance datasets from Huawei Munich Research Center for elevator industry; operation time series sampled at 4Hz (16:30–23:30) using electromechanical sensors, humidity, and vibration.
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.
Published in October 2025, this dataset includes command logs and sensor feedback for an SLM-based assistant designed for IoT maintenance tasks. It bridges voice commands with shell-level operations in industrial environments, facilitating the training of AI assistants for hardware management.
CICIoT2023 is a large-scale, flow-based network traffic dataset capturing real-time benign and malicious communications in an IoT environment composed of 105 physical devices. The dataset captures traffic traces for 33 attack scenarios grouped into seven categories: DDoS, DoS, Reconnaissance, web-based attacks, brute-force attempts, spoofing, and Mirai malware.
The Industrial IoT Dataset (Synthetic) provides a large-scale simulation of sensor readings and operational metrics from machines deployed in a smart factory environment. It focuses on predictive maintenance and anomaly detection.
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.