计算机科学 ›› 2020, Vol. 47 ›› Issue (10): 63-68.doi: 10.11896/jsjkx.200600014
所属专题: 群智感知计算
贾玉福1, 李明磊1, 刘文平1, 胡胜红2, 蒋洪波3
JIA Yu-fu1, LI Ming-lei1, LIU Wen-ping1, HU Sheng-hong2, JIANG Hong-bo3
摘要: 利用智能手机跟踪分析WiFi环境中群体结构的动态变化是一种非侵扰感知技术的新思路。基于WiFi信号差异与节点距离间的关系,设计了一种WiFi相异度的计算方法,根据节点之间的WiFi相异度统计出相异度距离,再利用提出的GSGA-RSS算法迭代计算得到节点坐标,最后利用DBSCAN进行分层次群组结构分析。文中提出了一种基于质心的节点序列位均差表示方法,基于该方法对不同节点间距条件下的队列和环状结构群组进行了实验分析。实验结果表明:在组间最小间距5 m、组内最大间距3 m的条件下,所提方法能够以94%的精度识别出85%的群体;节点间距为0.5 m的队列的位均差约为0.5,节点间距为1 m的环状结构的位均差约为1。
中图分类号:
[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] | 谢文康, 樊卫北, 张玉杰, 徐鹤, 李鹏. ENLHS:一种基于抽样的Kafka自适应调优方法 ENLHS:Sampling Approach to Auto Tuning Kafka Configurations 计算机科学, 2020, 47(8): 119-126. https://doi.org/10.11896/jsjkx.200300010 |
[2] | 徐新黎,陈琛,皇甫晓洁,崔永婷. 能量受限的单移动设备无线充电调度算法 Wireless Charging Scheduling Algorithm of Single Mobile Vehicle with Limited Energy 计算机科学, 2018, 45(3): 108-114. https://doi.org/10.11896/j.issn.1002-137X.2018.03.018 |
[3] | . 移动互联网访问控制支持移动感知的研究 计算机科学, 2006, 33(5): 52-55. |
|