University Building Recognition Dataset in Thailand for the mission-oriented IoT sensor system
This provides a specialized dataset for building recognition in Thailand, but it is incremental as it adapts an existing approach to a new location.
The paper tackles the need for mission-specific datasets in wireless ad hoc federated learning (WAFL) systems by developing the Chulalongkorn University Building Recognition Dataset (CUBR) for a case study in Thailand, and it demonstrates that training in WAFL scenarios achieves better accuracy than self-training scenarios.
Many industrial sectors have been using of machine learning at inference mode on edge devices. Future directions show that training on edge devices is promising due to improvements in semiconductor performance. Wireless Ad Hoc Federated Learning (WAFL) has been proposed as a promising approach for collaborative learning with device-to-device communication among edges. In particular, WAFL with Vision Transformer (WAFL-ViT) has been tested on image recognition tasks with the UTokyo Building Recognition Dataset (UTBR). Since WAFL-ViT is a mission-oriented sensor system, it is essential to construct specific datasets by each mission. In our work, we have developed the Chulalongkorn University Building Recognition Dataset (CUBR), which is specialized for Chulalongkorn University as a case study in Thailand. Additionally, our results also demonstrate that training on WAFL scenarios achieves better accuracy than self-training scenarios. Dataset is available in https://github.com/jo2lxq/wafl/.