Hourly air pollutant measurements (PM2.5, PM10, SO₂, NO₂, CO, O₃) and meteorological data from 12 nationally controlled monitoring sites in Beijing, collected from March 2013 to February 2017 (420,768 instances).[page:1][web:74]
Comprehensive smart energy and environment dataset from three real homes plus a microgrid of 400+ anonymous homes, including electricity usage/generation, environmental conditions, and operational events.[web:97][web:94][web:103]
City-scale environmental IoT dataset with more than 24 million temperature measurements from SmartSantander sensors deployed across Santander, Spain, including spatial, temporal, and device metadata.[web:113][web:115][web:118]
Real-world hourly air quality measurements from an array of chemical gas sensors deployed at road level in a polluted Italian city, collected from March 2004 to February 2005.[web:46][web:53][web:77]
Large-scale real-time air quality monitoring dataset from Dhaka, Bangladesh with 155,406 records. Captures CO, NO2, SO2, O3, PM2.5, and PM10 using IoT sensors with Arduino integration. Ideal for environmental analytics, pollution prediction, and smart city air quality management.
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.
Multi-source healthcare dataset integrating Electronic Health Records, medical imaging (CT and MRI scans), and wearable IoT sensor data for personalized treatment optimization. Includes 5,008 brain imaging files and real-time physiological monitoring data.
Specialized dataset for detecting IoT botnet attacks using network traffic analysis. Captures behavior of 9 real IoT devices infected with Mirai and BASHLITE malware variants. Ideal for training ML models to identify compromised IoT devices through traffic patterns.
Comprehensive large-scale IoT botnet dataset combining legitimate IoT network traffic with realistic botnet attack scenarios. Features full packet captures (PCAP) and extracted flow features for diverse attack types including DDoS, reconnaissance, theft, and DoS attacks.
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.
Comprehensive dataset capturing cybersecurity threats and sustainability metrics in smart city IoT and edge networks, including communication behavior, energy consumption patterns, and attack scenarios.
1,000 records of simulated IoT network activity with blockchain-based security. Covers DDoS, malware, MITM attacks across device, network, and application layers.