Computer Science ›› 2017, Vol. 44 ›› Issue (8): 64-70.doi: 10.11896/j.issn.1002-137X.2017.08.012

Previous Articles     Next Articles

Crowdsourcing-based Indoor Localization via Embedded Manifold Matching

ZHOU A-peng, QIN Xi-zhong, JIA Zhen-hong and NIKOLA Kasabov   

  • Online:2018-11-13 Published:2018-11-13

Abstract: With the boom of pervasive applications,indoor localization becomes more and more important.The traditionalfingerprint based positioning method requires the site survey in which the required time and workload are huge and the real-time updating needs to adapt to the changes in the room.All these factors greatly limit its scope of application.Therefore,the form of crowdsourcing was utilized to collect indoor information and record a large number of path information.The consistency of the low dimensional embedded manifold in the path were used for geographical position matching in order to establish the database of location fingerprints.Gauss particle filter denoising sensor data were used to further solve the problem of pedestrian step difference.According to the continuity of the user’s location and the path information,the reasonable nearest neighbor points were selected,and the accurate positioning was realized.The experiments have been carried out in the meeting room of 84m2,and the experiments can achieve comparable accuracy to the traditional method.The proposed method can adapt to the environmental changes in real time,and the positioning accuracy is better than the traditional positioning method after 2 weeks and even after 1 month.

Key words: Indoor localization,Crowdsourcing,Embedded manifold,Gauss particle filter,Pedestrian step difference

[1] XI R,LI Y J,HOU M S.The review of indoor positioning me-thods[J].Computer Science,2016,3(4):1-6.(in Chinese) 席瑞,李玉军,侯孟书.室内定位方法综述[J].计算机科学,2016,43(4):1-6.
[2] 杨峥,吴陈沭,刘云浩.位置计算:无线网络定位与可定位性[M].北京:清华大学出版社,2014:1-3.
[3] WU C,YANG Z,LIU Y.Smartphones based Crowdsourcing for Indoor Localization[J].IEEE Transactions on Mobile Computing,2015,14(2):444-457.
[4] YANG Z,WU C,LIU Y.Locating in fingerprint space:wireless indoor localization with little human intervention[C]∥International Conference on Mobile Computing and Networking.ACM,2012:269-280.
[5] WU C,YANG Z,XU Y,et al.Human Mobility Enhances Global Positioning Accuracy for Mobile Phone Localization[J].IEEE Transactionson Parallel & Distributed Systems,2015,6(1):131-141.
[6] YANG Z,WU C,ZHOU Z,et al.Mobility Increases Localiza-bility:A Survey on Wireless Indoor Localization using Inertial Sensors[J].ACM Computing Surveys,2015,47(3):1-34.
[7] LEU J S,YU M C,TZENG H J.Improving indoor positioning precision by using received signal strength fingerprint and footprint based on weighted ambient Wi-Fi signals[J].Computer Networks,2015,91:329-340.
[8] WU C,YANG Z,XIAO C,et al.Static power of mobile devices:Self-updating radio maps for wireless indoor localization[C]∥Computer Communications.IEEE,2015:2497-2505.
[9] ZHU J Y,ZHENG A X,XU J,et al.Spatio-temporal (S-T) simi- larity model for constructing WIFI-based RSSI fingerprinting map for indoor localization[C]∥International Conference on Indoor Positioning and Indoor Navigation.IEEE,2014:678-684.
[10] CHANG L,CHEN X,WANG J,et al.TaLc:Time Adaptive Indoor Localization with Little Cost[C]∥ACM MOBICOM Workshop on Challenged Networks.ACM,2015:461-490.
[11] WANG J,FANG D,CHEN X,et al.LCS:Compressive sensing based device-free localization for multiple targets in sensor networks[J].Proceedings-IEEE INFOCOM,2013,12(11):145-149.
[12] NIU J,WANG B,SHU L,et al.ZIL:An Energy-Efficient Indoor Localization System Using ZigBee radio to Detect WiFi Fingerprints[J].IEEE Journal on Selected Areas in Communications,2015,33(7):1431-1442.
[13] TORRES-SOSPEDRA J,MONTOLIU R,TRILLES S,et al.Comprehensive Analysis of Distance and Similarity Measures for Wi-Fi Fingerprinting Indoor Positioning Systems[J].Expert Systems with Applications,2015,42(23):9263-9278.
[14] GAO Y,YANG Q,LI G,et al.XINS:the anatomy of an indoor positioning and navigation architecture[C]∥Proceedings of the 1st International Workshop on Mobile Location-based Service.ACM,2011:41-50.
[15] HE S,CHAN S H G.Wi-Fi Fingerprint-based Indoor Positioning:Recent Advances and Comparisons[J].IEEE Communications Surveys & Tutorials,2015,18(1):466-490.
[16] LYMBEROPOULOS D,LIU J,YANG X,et al.A realistic eva- luation and comparison of indoor location technologies:expe-riences and lessons learned[C]∥The ACM/IEEE Conference on Information Processing in Sensor Networks.2015:178-189.
[17] Microsoft Indoor Localization Competition IPSN2015/2016[EB/OL].http://research.microsoft.com/en-us/events/indoorlocalizationcompetition 2015/default.aspx.
[18] LUNGA D,PRASAD S,CRAWFORD M M,et al.Manifold-Learning-Based Feature Extraction for Classification of Hyperspectral Data:A Review of Advances in Manifold Learning[J].IEEE Signal Processing Magazine,2014,31(1):55-66.
[19] ZHANG Z,WANG J,ZHA H.Adaptive manifold learning[J].IEEE Transactions on Pattern Analysis & Machine Intelligence,2012,34(2):253-265.
[20] FLOYD R W.Algorithm 97:Shortest path[J].Communications of the ACM,1962,5(6):345.
[21] WU C,YANG Z,LIU Y,et al.WILL:Wireless Indoor Localization without Site Survey[J].IEEE Transactions on Parallel & Distributed Systems,2013,24(4):839-848.
[22] HARLE R.A Survey of Indoor Inertial Positioning Systems for Pedestrians[J].Communications Surveys & Tutorials IEEE,2013,15(3):1281-1293.
[23] XIAO Y L,ZHANG S G,WANG J X.An indoor localization algorithm based on multidimensional scaling and region refinement[J].Chinese Journal of Computers,2016,39(67):6-8.(in Chinese) 肖亚龙,张士庚,王建新.一种基于多维标度和区域细化的无线室内定位方法[J].计算机学报,2016,39(67):6-8.
[24] GONZLEZ M C,HIDALGO C A,B ARABSI A L.Understanding individual human mobility patterns[J].Nature,2008,453(7196):779-782.

No related articles found!
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
No Suggested Reading articles found!