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.
Novel dataset combining IoT environmental sensors with robotic vision for automated plant disease detection. Published in Nature Scientific Reports January 2026. Features leaf images, environmental parameters, and deep learning disease classification with 98.9% accuracy.
Dataset for evaluating federated learning approaches to IoT intrusion detection published in Nature Scientific Reports January 2026. Features distributed network traffic from multiple IoT deployments with privacy constraints and decentralized learning evaluation metrics.
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 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.
Real-world IoT sensor dataset for precision agriculture and plant health monitoring. Includes environmental parameters (temperature, humidity, light) and soil metrics (pH, moisture, temperature) with Arduino-ESP8266 integration and cloud transmission.
Enhanced smart home energy consumption dataset with minute-resolution monitoring of 13+ appliances and regional weather data. Includes traditional appliances plus new IoT devices like car chargers, water heaters, pool pumps, and outdoor lighting.
New realistic IoT network intrusion dataset (MU-IoT) with comprehensive attack scenarios for cybersecurity research. Published in IEEE 2024 with 4+ citations. Covers multiple IoT protocols and device types.
Dataset with 500 controlled simulation scenarios analyzing ICSHSO-based dynamic optimization for energy efficiency in wireless sensor networks. Includes network lifetime, PDR, residual energy, and transmission reduction metrics.
Curated IoT network traffic dataset for intelligent network management and resource allocation research. Features diverse device types, traffic patterns, and quality-of-service metrics for ML-based optimization.
Comprehensive network traffic dataset from UNSW Canberra Cyber Range Lab capturing benign and malicious flows in simulated IoT/IIoT smart environments using Argus and Zeek (Bro) tools.