A multimodal dataset for environmental occupancy detection

This article presents a multimodal dataset collected in a real smart environment located at the Pontifical Catholic University of Rio Grande do Sul (PUCRS), Brazil. The dataset documents environmental, electrical, and device-interaction measurements collected from a heterogeneous Internet of Things (IoT) deployment composed of commercial smart devices and a custom ESP32-based sensing node. Environmental variables include carbon dioxide concentration, temperature, relative humidity, light intensity, and sound pressure level. Electrical measurements include instantaneous power, voltage, current, and device state indicators collected from smart sockets, switches, and a dedicated server. Additional metadata include device status indicators. The dataset also includes a human-verified ground_truth variable indicating the number of occupants in the room at each timestamp. This variable was produced through a two-stage annotation procedure in which YOLO26m detections were used as initial occupancy count suggestions and all corresponding camera images were subsequently reviewed manually to confirm or correct the final labels. Data were collected continuously under routine academic workspace operation, reflecting natural occupancy fluctuations, restricted access control, and heterogeneous network conditions. The custom sensing node was programmed in Arduino C++ and exposed measurements via HTTP requests, while commercial devices were polled through the local Tuya LAN protocol using the TuyAPI library. All records were stored in structured JSON format using a common timestamp assigned at the end of each acquisition cycle. Each record represents the aggregation of values polled within the same 10 s collection window, rather than perfectly simultaneous measurements acquired at the exact same instant. The dataset is publicly available in a repository and includes raw measurements, device logs, and documentation describing hardware configuration, and acquisition procedures. These data can support research in occupancy detection, energy usage analysis, anomaly detection, and smart-building experimentation. The dataset also provides a documented example of a real-world IoT deployment suitable for replication or comparative studies.