Synthetic IIoT dataset reflecting real milling machine predictive maintenance scenarios. 10,000 records with 14 features including air temperature, process temperature, rotational speed, torque, and 5 labeled failure types. CSV format. Ideal for multi-label fault classification.
One of Kaggle's largest IIoT manufacturing datasets with 1.18 million parts measured across Bosch's assembly lines. Thousands of anonymized sensor features split across numeric, categorical, and date files. CSV format. Used for quality control and failure prediction.
Wearable IoT dataset with 18 physical activities from 9 subjects wearing 3 IMUs and a heart rate monitor. 54 columns including temperature, acceleration, and gyroscope data. CSV format. Used for HAR, activity classification, and intensity estimation.
Real-world wearable dataset from 15 nurses over one week in a hospital. Contains 11.5 million entries of EDA, heart rate, skin temperature, and orientation data collected via Empatica E4. CSV format. Used for occupational stress detection research.
Residential smart-home power dataset with 2,075,259 one-minute measurements from a French household over 47 months. TXT/CSV-style tabular format. Used for load forecasting, NILM, and energy behavior analysis.
Long-duration smart-home utility dataset with two years of minutely electricity, water, and natural gas measurements plus weather and billing data. CSV/TSV/RData formats. Used for forecasting, NILM, and resource analytics.
Appliance-level and aggregate electricity dataset from 20 UK households sampled every 8 seconds. CSV files, one per home. Built for energy conservation, NILM, demand response, and smart-home automation research.
Network-traffic dataset on Mendeley Data documenting DDoS attacks against the Fibaro Home Center 3 smart-home controller; PCAP and CSV formats are provided. [page:4][web:52]
A real-world cybersecurity dataset capturing MQTT-based IoT network traffic with live attacks and anomalous behavior. Collected from an active deployment with multiple attack types including DoS, SlowITe, and malformed injections. Provides both raw and preprocessed CSV files with rich metadata for intrusion detection and anomaly classification research.
IoT-DH is a real-world IoT DDoS honeypot dataset collected from a honeypot deployment and converted from PCAP to CSV with traffic features and labels for DDoS classification, identification, and detection tasks.