Computer Science ›› 2020, Vol. 47 ›› Issue (10): 63-68.doi: 10.11896/jsjkx.200600014

Special Issue: Mobile Crowd Sensing and Computing

• Mobile Crowd Sensing and Computing • Previous Articles     Next Articles

Group Perception Analysis Method Based on WiFi Dissimilarity

JIA Yu-fu1, LI Ming-lei1, LIU Wen-ping1, HU Sheng-hong2, JIANG Hong-bo3   

  1. 1 School of Information Management and Statistics,Hubei University of Economics,Wuhan 430205,China
    2 School of Information Engineering,Hubei University of Economics,Wuhan 430205,China
    3 College of Computer Science and Electronic Engineering,Hunan University,Changsha 410082,China
  • Received:2020-05-31 Revised:2020-09-02 Online:2020-10-15 Published:2020-10-16
  • About author:JIA Yu-fu,born in 1974,Ph.D,lecturer,is a member of China Computer Federation.His main research interests include intelligent perception and mobile computing.
    LI Ming-lei,born in 1982,Ph.D,lectu-rer.His main research interests include data analysis and data mining.
  • Supported by:
    National Natural Science Foundation of China (61672213),Natural Science Foundation of Hubei Province(2018CFB721) andResearch program of Science and Technology Department of Education Department of Hubei Province (D20182202)

Abstract: It is a new idea of non-intrusive perception technology to track and analyze the dynamic change of group structure in WiFi environment by using smart phone.Based on the relationship between WiFi information difference and between-user distance,a method of WiFi dissimilarity computation is designed.According to the WiFi dissimilarity between users,the dissimilarity distance is statistically calculated,and then the GSGA-RSS algorithm is used to iteratively calculate the node coordinates.Finally,the hierarchical group structure is analyzed by DBSCAN.A method of LMD (location mean deviation) computation based on mass center is proposed,and experiments on groups structures of queues and ring topology under different between-user distances are conducted.The results show that the proposed approach can identify 85% of the groups with 94% precision for the cases with the minimum intergroup distance of 5 m and the maximum intragroup distance of 3 m.The LMD is about 0.5 for the queues with between-user distance of 0.5 m,and about 1 for the ring structure with between-user distance of 1 m.

Key words: Elastic network, Group structure, Location mean deviation, Mobile sensing, WiFi dissimilarity

CLC Number: 

  • TP391.4
[1]YU Z W,WANG Z.Human Behavior Analysis:Sensing and Understanding[M].Singapore:Springer,2020:139-218.
[2]BOUBICHE D E,IMRAN M,MAQSOOD A,et al.Mobilecrowd sensing-Taxonomy,applications,challenges,and solutions [J].Computers in Human Behavior,2019,101(12):352-370.
[3]YU N,HAN Q.Grace:Recognition of proximity-based inten-tional groups using collaborative mobile devices[C] //Procee-dings of the 2014 IEEE 11th International Conference on Mobile Ad Hoc and Sensor Systems.IEEE Computer Society,2014:10-18.
[4]WIRZ M,PSCHLÄPFER,KJAERGAARD M B.Towards an online detection of pedestrian flocks in urban canyons by smoothed spatio-temporal clustering of GPS trajectories[C]//Proceedings of the 3rd ACM SIGSPATIAL International Workshop on Location-Based Social Networks.Association for Computing Machinery,2011:17-24.
[5]SEN R,LEE Y,JAYARAJAH K,et al.GruMon:Fast and accurate group monitoring for heterogeneous urban spaces[C]//Proceedings of the 12th ACM Conference.Embedded Network.Sensor System.Association for Computing Machinery,2014:46-60.
[6]SANCHEZ-CORTES D,ARAN O,MAST M S,et al.Gatica-Perez,A nonverbal behavior approach to identify emergentlea-ders in small groups[J].IEEE Transactions on Multimedia,2012,14(3):816-832.
[7]KJAERGAARD M B,WIRZ M,ROGGEN D,et al.Mobile sensing of pedestrian flocks in indoor environments using WiFi signals[C]//Proceedings of the 2012 IEEE International Confe-rence on Pervasive Computing and Communications.Springer-Verlag,2012:95-102.
[8]COSTA M.Interpersonal distances in group walking[J].Journal of Nonverbal Behavior,2010,34(1):15-26.
[9]CHEN H,GUO B,YU Z W,et al.A generic framework for constraint-driven data selection in mobile crowd photographing[J].IEEE Internet of Things Journal,2017,4(1):284-296.
[10]LI Q,HAN Q,CHENG X,et al.Collaborative Recognition of Queuing Behavior on Mobile Phones[J].IEEE Transactions on Mobile Computing,2016,15(1):60-73.
[11]WU F,SOLMAZ G.Are you in the line? rssi-based queue detection in crowds[C]//Proceedings of the 2017 IEEE International Conference on Communications.IEEE Communications Society,2017:21-25.
[12]DU H,YU Z W,YI F,et al.Recognition of group mobility level and group structure with mobile devices[J].IEEE Transactions on Mobile Computing,2018,17(4):884-897.
[13]DU H,YU Z W,YI F,et al.Group mobility classification and structure recognition using mobile devices[C]//Proceedings of the 2016 IEEE International Conference on Pervasive Computing and Communications.IEEE computer Society,2016:1-9.
[14]KJAERGAARD M B,BLUNCK H,WÜSTENBERG M.Time-lag method for detecting following and leadership behavior of pedestrians from mobile sensing data[C]//Proceedings of the IEEE International Conference on Pervasive Computing and Communications.IEEE computer Society,2013:18-22.
[15]KJAERGAARD M B,WIRZ M,ROGGEN D.Detecting pedestrian flocks by fusion of multi-modal sensors in mobile phones[C]//Proceedings of the Acm Conference on Ubiquitous Computing,September.Association for Computing Machinery,2012:240-249.
[16]YU Z,XU H,YANG Z,et al.Personalized travel package with multi-point-of-interest recommendation based on crowdsourceduser footprints[J].IEEE Transactions on Human-Machine Sys-tems,2016,46(1):151-158.
[17]LI Q,HAN Q,CHENG X,et al.Collaborative Recognition of Queuing Behavior on Mobile Phones[J].IEEE Transactions on Mobile Computing,2016,15(1):60-73.
[18]XU E,YUZ W,DU H,et al.User profile system based on mobile sensing data [J].Journal of Zhengzhou University(Natural Science Edition),2019(4):30-36.
[19]RAY A,MALLICK S,MONDAL S,et al.A Framework forMobile Crowd Sensing and Computing based Systems[C]//Proceedings of the 2018 IEEE International Conference on Advanced Networks and Telecommunications Systems (ANTS).2018:1-6.
[20] LIU W P,JIA Y F,JIANG G Y,et al.WiFifi-sensing based person-to-person distance estimation using deep learning[C]//Proceedings of the 2018 IEEE 24th International Conference on Parallel and Distributed Systems (ICPADS).2018:236-243.
[21]SHEN G B,CHEN Z,ZHANG P C.Walkie-Markie:indoorpathway mapping made easy[C] //Proceedings of the 10th USENIX conference on Networked Systems Design and Implementation.USENIX Association,2013:85-98.
[22]DABEK F,COX R,KAASHOEK F,et al.Vivaldi:A decentralized network coordinate system[C]//Proceedings of the 2004 Conference on Applications,Technologies,Architectures,and Protocols for Computer Communications.Association for Computing Machinery,2004:15-26.
[23]HOWARD A,MATARIC M,SUKHATME G.Relaxation on a mesh:a formalism for generalized localization[C] //Proceedings 2001 IEEE/RSJ International Conference on Intelligent Robots and Systems.IEEE Robotics & Automation Magazine,2001:1055-1060.
[24]PRIYANTHA N B,BALAKRISHNAN H,DEMAINE E,et al.Anchor-free distributed localization in sensor networks[R].Technical Report,MIT CSail,2003.
[25]YEDAVALLI K,KRISHNAMACHARI B,RAVULA S,et al.Ecolocation:A technique for RF based localization in wireless sensor networks[C] //Proceedings of Information Processing in Sensor Networks.IEEE Signal Processing Society,2005:285-292.
[1] XU Xin-li, CHEN Chen, HUANGFU Xiao-jie and CUI Yong-ting. Wireless Charging Scheduling Algorithm of Single Mobile Vehicle with Limited Energy [J]. Computer Science, 2018, 45(3): 108-114.
[2] XIONG Ying,SHI Dian-xi,DING Bo and DENG Lu. Survey of Mobile Sensing [J]. Computer Science, 2014, 41(4): 1-8.
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
No Suggested Reading articles found!