Improving face-to-machine proximity estimation with neighbor relations in mobile industrial human machine interaction

Rongkai Wang, Zhezhuang Xu, Renxu Xie, Xing Liu, Shih-Hau Fang

Abstract: In the mobile industrial human machine interaction (HMI), the engineer has to manually select the target machine from a list to establish the data connection. It is a nontrivial problem since there are more machines connected to the industrial cyber-physical systems, and the list becomes so long that is hard to be read. Considering the characteristic that the industrial HMI is typically executed in a face-to-machine manner, the face-to-machine proximity estimation (FaceME) algorithm has been proposed to simplify the manual selection. However, due to the limited utilization of RSSI data, the estimation accuracy of FaceME is not sufficient in the application with densely deployed machines. In this paper, we propose the model to formulate the neighbor relations, and then design a new face-to-machine proximity estimation algorithm with neighbor relations (FaceMEN) to improve the estimation accuracy. The experimental results prove that FaceMEN greatly improves the estimation accuracy and time complexity.

External IDs:dblp:conf/icphys/WangXXLF18