pNEUMA is a large-scale dataset of naturalistic vehicle trajectories from half a million vehicles in Athens, Greece, collected via a swarm of drones for traffic congestion research.
A real-world, hourly dataset of electricity, heating, and cooling consumption for a commercial building, ideal for benchmarking and building energy model calibration.
Comprehensive smart home dataset with 1,048,575 rows and 31 columns including timestamp, device states (TV, oven, lights, fridge) and activity labels for machine learning applications.
Sensor recordings of activities by a single user in a smart home, using PIR, reed switches, force sensors, light, temperature/humidity and smart plugs; includes three CSV files for analysis.
A dataset capturing real-time metrics of resource allocation and workload distribution across multi-tier IoT architectures. Includes latency, CPU and memory usage, task execution times, and predictive performance variables, enabling research in IoT resource management, edge analytics and performance optimization.
Longitudinal ambient sensor data collected from 4 community homes featuring motion sensors, door sensors, and temperature readings with activity labels. This dataset captures naturalistic behavior patterns for activity recognition model development in real-world smart home environments. Data includes continuous recordings from PIR motion sensors, magnetic door sensors, and ambient temperature sensors with annotated activities by external annotators. The dataset provides a valuable resource for building activity recognition models that operate in uncontrolled, naturalistic settings.
AIR4LIFE is a high-resolution air quality monitoring dataset published in late 2025, featuring measurements from a dual-node IoT setup. It tracks pollutants and ambient conditions to evaluate the trade-offs between energy-aware duty cycling and data completeness in environmental sensing networks.