A synthetic but carefully constructed IoT dataset for smart home renewable energy management, providing five CSV datasets (20, 50, 100, 200 homes over 365 days) with daily energy consumption and production values to simulate small, medium, and large-scale smart city and smart home scenarios.
An image dataset of 3,200 manually annotated RGB images covering 32 classes of IoT education kits (Arduino, ESP32, Raspberry Pi, etc.), collected from multiple sources and annotated with polygon masks via Roboflow for object detection research in educational IoT kits.
A merged and optimized dataset combining N-BaIoT (IoT-specific traffic) and UNSW-NB15 (general network threats) with feature engineering, dimensionality reduction, and benchmarked ML models (Decision Tree, SVM, Random Forest, Neural Network) for IoT anomaly detection, published in Scientific Reports.
A federated learning evaluation across several contemporary IoT and IIoT intrusion detection datasets, benchmarking algorithms such as FedAvg, FedProx, and FedNova with LSTM and Transformer models in in-domain, cross-dataset, and multi-dataset federation scenarios.
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.
A pair of labeled MQTT and UDP DDoS datasets for Healthcare-IoT networks, generated with Cooja and ns-3 simulators to support evaluation of DDoS detection and mitigation techniques in H-IoT environments.
A real-world IoT dataset from a multi-purpose university building at University of Sharjah, capturing appliance-level energy consumption, temperature, humidity, and occupancy, along with 2D Markov Transition Field (MTF) image representations for deep learning, published in Data in Brief.
Detailed energy monitoring dataset from smart home testbed with five common household appliances (refrigerator, washing machine, microwave, air conditioner, TV) each connected to individual smart meters for appliance-level consumption analysis and NILM research.
Standardized energy flexibility data model enabling uniform communication of energy flexibility potentials within industrial companies and in exchange with external energy systems (grids, aggregators), supporting demand response and sector coupling.
Deep learning-ready dataset combining real vehicle CAN bus traffic and simulated attack scenarios (DoS, fuzzing, spoofing) for training intrusion detection systems to protect autonomous and connected vehicles from cyber-attacks.
Multimodal dataset combining Text-to-SQL natural language queries with IoT network traffic classification, featuring 10,985 SQL training examples and labeled network traffic (benign/malicious) from IoT-23 and Smart Building sensors for NLP and security research.
Curated collection of 10,000+ realistic IoT sensor readings from Bangladesh representing diverse environmental conditions including temperature, humidity, soil moisture, rainfall, and air quality with timestamps and location tags for smart agriculture and climate research.