Multimodal dataset from 18 subjects with wearable IMUs (wrists, torso), Bluetooth indoor localization, and first-person video capturing 12 activity categories in realistic home/office scenarios for human activity recognition and fall detection research.
Dataset from wearable system integrating triaxial accelerometers with flexible haptic actuators for real-time body motion sensing and synchronized vibration feedback, including ML-classified motion data from diverse body placements for VR/AR and remote communication applications.
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.
Comprehensive collection of research papers on IoT attacks and security models published between 2005-2025 in IEEE, Elsevier, Springer, ACM, and MDPI, compiled for systematic review of IoT network security across all layers, enabling meta-analysis and trend identification.
Comprehensive Industrial IoT security dataset from the Canadian Institute for Cybersecurity, featuring realistic network traffic with 34 types of attacks including DDoS, ransomware, data exfiltration, and advanced persistent threats across multiple IIoT protocols.
Continuous 4-week wearable dataset from 49 participants with smartwatch-based heart rate variability (HRV), motion sensors, daily sleep diaries, and biweekly clinical assessments of depression, anxiety, and insomnia for mental health research.
Novel dataset correlating real wearable device data (heart rate, steps, sleep, calories) with Self-Reported Quality of Life (SRQoL) measures using the WHOQOL-BREF questionnaire for health monitoring and QoL prediction research.
Spatio-temporal water quality dataset from 36 monitoring sites in Georgia, USA, with 11 indices including dissolved oxygen, temperature, conductance, and pH for daily forecasting of pH levels.
Real-world parking occupancy and capacity data from NCP-operated car parks in Birmingham, UK, collected every 30 minutes from October to December 2016 for time-series occupancy prediction.
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]
Real in-vehicle CAN bus traffic from a Kia Soul logged via OBD-II port, including normal operation and three types of message injection attacks (DoS, fuzzy, impersonation) for intrusion detection research.[web:224][web:225][web:221]
Field data from an automated irrigation setup using capacitive soil moisture sensors and DHT‑11 air sensors, recording soil moisture, air temperature, humidity, and pump on/off status for smart irrigation control.[web:156]