FaceME: Face-to-machine proximity estimation based on RSSI difference for mobile industrial human-machine interaction - Details

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Abstract：

In　the　mobile　industrial　human-machine　interaction　(HMI),　to　establish　the　data　connection,　the　engineer　has　to　manually　select　the　target　machine　from　a　long　list,　which　may　lead　to　wrong　connection　and　waste　of　time.　We　observe　that　the　engineer　should　face　to　the　machine　during　the　interaction　to　ensure　that　the　machine　works　accurately,　and　this　characteristic　makes　the　proximity　estimation　algorithm　suitable　to　simplify　the　data　connection.　However,　due　to　the　densely　deployed　machines,　the　existing　algorithms　cannot　provide　sufficient　accuracy　with　limited　latency.　In　this　paper,　we　implement　a　testbed　to　evaluate　the　performance　in　the　mobile　industrial　HMI.　Based　on　the　experimental　results,　we　propose　the　definition　of　received　signal　strength　indicator　(RSSI)　difference　and　then　use　it　to　design　the　face-to-machine　proximity　estimation　(FaceME)　algorithm.　The　experimental　results　prove　that　FaceME　can　provide　guaranteed　estimation　accuracy　and　low-time　complexity.　©　2005-2012　IEEE.

Keyword：

Industrial internet of things (IIoT) Man machine systems Wireless sensor networks

Community：

[ 1 ] [Xu, Zhezhuang]School of Electrical Engineering and Automation, Fuzhou University, Fuzhou; 350000, China
[ 2 ] [Xu, Zhezhuang]Key Laboratory of Industrial Automation Control Technology and Information Processing, Education Department of Fujian Province, Fuzhou; 350000, China
[ 3 ] [Wang, Rongkai]School of Electrical Engineering and Automation, Fuzhou University, Fuzhou; 350000, China
[ 4 ] [Wang, Rongkai]Key Laboratory of Industrial Automation Control Technology and Information Processing, Education Department of Fujian Province, Fuzhou; 350000, China
[ 5 ] [Yue, Xi]School of Electrical Engineering and Automation, Fuzhou University, Fuzhou; 350000, China
[ 6 ] [Yue, Xi]Key Laboratory of Industrial Automation Control Technology and Information Processing, Education Department of Fujian Province, Fuzhou; 350000, China
[ 7 ] [Liu, Ting]School of Electrical Engineering and Automation, Fuzhou University, Fuzhou; 350000, China
[ 8 ] [Liu, Ting]Key Laboratory of Industrial Automation Control Technology and Information Processing, Education Department of Fujian Province, Fuzhou; 350000, China
[ 9 ] [Chen, Cailian]Department of Automation, Shanghai Jiao Tong University, Shanghai; 200240, China
[ 10 ] [Fang, Shih-Hau]Department of Electrical Engineering, Yuan Ze University, Chung-Li; 32003, Taiwan
[ 11 ] [Fang, Shih-Hau]MOST Joint Research Center for AI Technology and All Vista Healthcare, Taoyuan; 320, Taiwan

Reprint 's Address：

[xu, zhezhuang]school of electrical engineering and automation, fuzhou university, fuzhou; 350000, china;;[xu, zhezhuang]key laboratory of industrial automation control technology and information processing, education department of fujian province, fuzhou; 350000, china

Email：

zzxu@fzu.edu.cn

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Source ：

IEEE Transactions on Industrial Informatics

ISSN： 1551-3203

Year： 2018

Issue： 8

Volume： 14

Page： 3547-3558

7 . 3 7 7

JCR@2018

1 1 . 7 0 0

JCR@2023

ESI HC Threshold：170

JCR Journal Grade：1

CAS Journal Grade：1

Cited Count：

WoS CC Cited Count：

SCOPUS Cited Count： 40

ESI Highly Cited Papers on the List： 0 Unfold All

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Chinese Cited Count：

30 Days PV： 0

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