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.
Large-scale reproducible IoT network dataset with traffic from 100+ diverse IoT devices including smart home, wearable, and industrial sensors, featuring multiple attack scenarios and benign behavior for intrusion detection research.
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.
Multimodal smart city dataset combining IoT sensor data (environmental, traffic, infrastructure) with ground-truth anomaly labels for urban safety applications, anomaly detection, and multi-source data fusion research in smart cities.
Complete and labeled IoT dataset from physical data center with NFC smart passive temperature sensors, containing normal operations and anomalous behaviors for time-series anomaly detection in critical infrastructure environments.
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.
Continuous 7-day water quality monitoring from a fish pond using Arduino-based digital sensors measuring temperature, pH, and turbidity at two depths (30 cm and 60 cm), with 9,623 minute-resolution records.