Real-time adaptive data filtering with multiple sensors for indoor monitoring
Kuon Akiyama, Ryoichi Shinkuma, Jun Shiomi · NOMS 2022-2022 IEEE/IFIP Network Operations and Management Symposium · 2022
This demonstration proposes a scheme that suppresses the data size of a 3D-image sensing network by adaptively filtering low-importance points, such as the points of floors and ceilings when the task is to track pedestrians. The adaptive filter can be dynamically changed to further reduce the amount of data if it is difficult for packets to reach the edge computer. We evaluate the proposed scheme through experiments and demonstrate that it performs better than benchmark schemes in terms of prompt arrival of the data.