With the pandemic of COVID-19, indoor crowd density monitoring has become one of the most critical responsibilities of public space managers. The University of Tokyo released an indoor density monitoring system using BLE beacons to monitor the indoor crowd density. In order to obtain accurate indoor crowd density monitoring results, we carefully designed beacon placement to cover the spatial area.

Beacon placement optimization has been studied for years. Some researchers proposed batch simulation-based sensor placement optimization methods to obtain the optimal placement by simulating radio wave propagation of beacons. However, the only simulation cannot reflect the actual radio map in the target environment. Some other research proposed optimization methods by selecting beacons from large distributed beacons. This approach can provide the beacon placement considering actual radio propagation. However, it demands an ideal initial beacon placement of adequate beacons and dense data measurement.

In this project, we propose the method of adaptive incremental beacon placement optimization (AI-BPO), which incrementally determines the new beacon placement location after gathering RSSI data in the environment with less labor cost. We experimented with the university buildings, and the experimental results show the effectiveness of our proposed method.

—– Publication —–

Yang Zhen, Masato Sugasaki, Yoshihiro Kawahara, Kota Tsubouchi, Masamichi Shimosaka.
Incremental BLE beacon placement optimization for crowd density monitoring applications
情報処理学会研究報告 第70回UBI合同研究発表会, Virtual, 6 2021.

Yang Zhen, Masato Sugasaki, Yoshihiro Kawahara, Kota Tsubouchi, Matthew Ishige, Masamichi Shimosaka
AI-BPO: Adaptive incremental BLE beacon placement optimization for crowd density monitoring applications
SIGSPATIAL’21: Proceedings of the 29th International Conference on Advances in Geographic Information Systems, November 2021, Beijing, China, November 2021.
Presentation on YouTube: https://www.youtube.com/watch?v=5qAT7qljAfc