Computer Science ›› 2018, Vol. 45 ›› Issue (10): 69-77.doi: 10.11896/j.issn.1002-137X.2018.10.014

• Network & Communication • Previous Articles     Next Articles

Incremental Indoor Localization for Device Diversity Issues

XIA Jun, LIU Jun-fa, JIANG Xin-long, CHEN Yi-qiang   

  1. University of Chinese Academy of Sciences,Beijing 100049,China
    Research Center for Ubiquitous Computing Systems,Institute of Computing Technology,Chinese Academy of Sciences,Beijing 100190,China
    Beijing Key Laboratory of Mobile Computing and Pervasive Device,Beijing 100190,China
  • Received:2018-03-03 Online:2018-11-05 Published:2018-11-05

Abstract: With the rapid development of Wireless Local Area Network(WLAN),the Received Signal Strength(RSS) based indoor localization becomes a hot area in research and application fields.Among various kinds of up-to-date indoor localization methods,fingerprint based methods are most widely used because of its good performance,and one feature of those methods is that the accuracy is determined by the identification of the training and the testing dataset.Howe-ver,in practical applications,there are three problems in existing fingerprint based methods or system.Firstly,the loca-lization error caused by device variance is a severe problem.Secondly,the wireless data are changing as the time passed,leading to the reduction of the prediction accuracy.Thirdly,traditional fingerprint based methods or system cannot avoid the dependency on a large amount of labeled data to keep effective positioning performance,which usually involves high cost in labor and time.To solve these problems,this paper proposed a incremental indoor localization method for device diversity issues,which keeps real-time update by training uncalibrated data that collected in localization.Experimental results show that the proposed method can increase the precision of overall indoor localization system,especially when error distance is between 3 and 5 meters.What’s more,this method possesses good advantage of timeliness compared with traditional indoor localization method on real BLE dataset.

Key words: Device diversity, Extreme learning machine, Incremental learning, Indoor localization, Internet of things

CLC Number: 

  • TP311
[1]WU T T,YUN Z,LIU Y,et al.BeiDou /GPS combination positioning methodology[J].Journal of Remote Sensing,2014,18(5):1087-1097.
[2]JAKUBSTREIT J.Summary of available indoor location techniques[J].IFAC-PapersOnLine,2016,49(25):311-317.
[3]ZHU J,LUO H,CHEN Z,et al.RSSI based Bluetooth low energy indoor positioning[C]∥International Conference on Indoor Positioning and Indoor Navigation.IEEE,2015:526-533.
[4]HE X,ALOI D N,LI J.Probabilistic Multi-Sensor Fusion Based Indoor Positioning System on a Mobile Device[J].Sensors,2015,15(12):31464-31481.
[5]LI W L,ILTIS R A,WIN M Z.A smartphone localization algorithm using RSSI and inertial sensor measurement fusion[C]∥Global Communications Conference.IEEE,2014:3335-3340.
[6]LUO Z J,WU W J,YANG M.MobileInternet:Terminal de- vices,networks and services[J].Chinese Journal of Computers,2011,34(11):2029-2051.(in Chinese)
罗舟军,吴文甲,杨明.移动互联网:终端、网络与服务[J].计算机学报,2011,34(11):2029-2051.
[7]ZHOU A Y,YANG B,JIN C Q,et al.Location-based services:Architecture and progress[J].Chinese Journal of Computers,2011,34(7):1155-1171.(in Chinese)
周傲英,杨彬,金澈清,等.基于位置的服务:架构与进展[J].计算机学报,2011,34(7):1155-1171.
[8]PAHLAVAN K,LI X,MAKELA J P.Indoor geolocation scien- ce and technology[M].IEEE Press,2002.
[9]GNTHER A,HOENE C.Measuring Round Trip Times to Determine the Distance Between WLAN Nodes[C]∥International Conference on Research in Networking.Springer Berlin Heidelberg,2005:768-779.
[10]HOENE C,WILLMANN J.Four-way TOA and software-based trilateration of IEEE 802.11 devices[C]∥International Symposium on Personal,Indoor and Mobile Radio Communications.IEEE,2008:1-6.
[11]LLOMBART M,CIURANA M,BARCELO-ARROYO F.On the scalability of a novel WLAN positioning system based on time of arrival measurements[C]∥Workshop on Positioning,Navigation and Communication,2008(Wpnc 2008).IEEE,2008:15-21.
[12]CIURANA M,BARCELO-ARROYO F,LLOMBART M.Im- proving the Performance of TOA Over Wireless Systems to Track Mobile Targets[C]∥IEEE International Conference on Communications Workshops,2009.IEEE,2009:1-6.
[13]SATHYAN T,HUMPHREY D,HEDLEY M.WASP:A System and Algorithms for Accurate Radio Localization Using Low-Cost Hardware[J].IEEE Transactions on Systems Man & Cybernetics Part C,2011,41(2):211-222.
[14]YOUSSEF M A,AGRAWALA A,SHANKAR A U.WLAN location determination via clustering and probability distributions[C]∥IEEE International Conference on Pervasive Computing and Communications.IEEE,2003:143-150.
[15]HE S,HU T,CHAN S H G.Contour-based Trilateration for In- door Fingerprinting Localization[C]∥ACM Conference on Embedded Networked Sensor Systems.ACM,2015:225-238.
[16]SJOBERG M,KOSKELA M,VIITANIEMI V,et al.Indoor location recognition using fusion of SVM-based visual classifiers[C]∥IEEE International Workshop on Machine Learning for Signal Processing.IEEE,2010:343-348.
[17]WU K,XIAO J,YI Y,et al.FILA:Fine-grained indoor localization[C]∥2012 Proceedings IEEE INFOCOM.IEEE,2012:2210-2218.
[18]SEN S,RADUNOVIC B,CHOUDHURY R R,et al.You are fa- cing the Mona Lisa:spot localization using PHY layer information[C]∥International Conference on Mobile Systems,Applications,and Services.ACM,2012:183-196.
[19]FANG S H,LIN T.Principal Component Localization in Indoor WLAN Environments[J].IEEE Transactions on Mobile Computing,2011,11(1):100-110.
[20]ZHENG V W,XIANG E W,YANG Q,et al.Transferring loca- lization models over time[C]∥National Conference on Artificial Intelligence.AAAI Press,2008:1421-1426.
[21]SUN Z,CHEN Y,QI J,et al.Adaptive Localization through Transfer Learning in Indoor Wi-Fi Environment[C]∥International Conference on Machine Learning and Applications.IEEE,2008:331-336.
[22]HAEBERLEN A,FLANNERY E,LADD A M,et al.Practical robust localization over large-scale 802.11wireless networks[C]∥ACM MOBICOM 2004,the 10th Annual International Conferences on Mobile Computing andNetworking.Philadelphia:ACM Press,2004:70-84.
[23]KJAERGAARD M B,MUNK C V.Hyperbolic location fingerprinting:A calibration-free solution for handling differences in signalstrength[C]∥Proc.of the IEEE Int’l Conf.on Pervasive Computing.Hong Kong:IEEE Press,2008:110-116.
[24]GU Y,JIANG X L,LIU J F,et al.Device adaptive wireless signal feature extraction and localization method[J].Journal of Software,2014,25(Suppl.(2)):12-20.(in Chinese)
谷洋,蒋鑫龙,刘军发,等.设备自适应的无线信号特征提取与定位方法[J].软件学报,2014,55(Suppl.(2)):12-20.
[25]TSUI A W,CHUANG Y H,CHU H H.Unsupervised Learning for Solving RSS Hardware Variance Problem in WiFi Localization[J].Mobile Networks and Applications,2009,14(5):677-691.
[26]HUANG G B,ZHU Q Y,SIEW C K.Extreme learning ma- chine:a new learning scheme of feedforward neural networks[C]∥IEEE International Joint Conference on Neural Networks,2004.IEEE,2005:985-990.
[27]HUANG G B,ZHU Q Y,SIEW C K.Extreme learning ma- chine:Theory and applications[J].Neurocomputing,2006,70(1-3):489-501.
[28]HUANG G B,CHEN L,SIEW C K.Universal approximation using incremental constructive feedforward networks with random hidden nodes[J].IEEE Transactions on Neural Networks,2006,17(4):879.
[29]HUANG G B,ZHOU H,DING X,et al.Extreme learning machine for regression and multiclass classification[J].IEEE Transactions on Systems Man & Cybernetics Part B Cybernetics A Publication of the IEEE Systems Man & Cybernetics Society,2012,42(2):513-529.
[30]CHUNG R K.Spectral graph theory[M].American Mathematical Society,1997.
[31]BELKIN M,MATVEEVA I,NIYOGI P.Regularization and Semi-supervised Learning on Large Graphs[M]∥Learning Theory.Springer Berlin Heidelberg,2004:624-638.
[32]BELKIN M,NIYOGI P,SINDHWANI V.Manifold Regularization:A Geometric Framework for Learning from Labeled and Unlabeled Examples[M].JMLR.org,2006.
[33]LIANG N Y,HUANG G B,SARATCHANDRAN P,et al.A fast and accurate online sequential learning algorithm for feedforward networks[J].IEEE Transactions on Neural Networks,2006,17(6):1411-1423.
[1] SHAO Zi-hao, YANG Shi-yu, MA Guo-jie. Foundation of Indoor Information Services:A Survey of Low-cost Localization Techniques [J]. Computer Science, 2022, 49(9): 228-235.
[2] LIU Dong-mei, XU Yang, WU Ze-bin, LIU Qian, SONG Bin, WEI Zhi-hui. Incremental Object Detection Method Based on Border Distance Measurement [J]. Computer Science, 2022, 49(8): 136-142.
[3] ZHANG Chong-yu, CHEN Yan-ming, LI Wei. Task Offloading Online Algorithm for Data Stream Edge Computing [J]. Computer Science, 2022, 49(7): 263-270.
[4] QING Chao-jin, DU Yan-hong, YE Qing, YANG Na, ZHANG Min-tao. Enhanced ELM-based Superimposed CSI Feedback Method with CSI Estimation Errors [J]. Computer Science, 2022, 49(6A): 632-638.
[5] ZHANG Xi-ran, LIU Wan-ping, LONG Hua. Dynamic Model and Analysis of Spreading of Botnet Viruses over Internet of Things [J]. Computer Science, 2022, 49(6A): 738-743.
[6] DONG Dan-dan, SONG Kang. Performance Analysis on Reconfigurable Intelligent Surface Aided Two-way Internet of Things Communication System [J]. Computer Science, 2022, 49(6): 19-24.
[7] Ran WANG, Jiang-tian NIE, Yang ZHANG, Kun ZHU. Clustering-based Demand Response for Intelligent Energy Management in 6G-enabled Smart Grids [J]. Computer Science, 2022, 49(6): 44-54.
[8] SHEN Shao-peng, MA Hong-jiang, ZHANG Zhi-heng, ZHOU Xiang-bing, ZHU Chun-man, WEN Zuo-cheng. Three-way Drift Detection for State Transition Pattern on Multivariate Time Series [J]. Computer Science, 2022, 49(4): 144-151.
[9] ZHANG Zhen-chao, LIU Ya-li, YIN Xin-chun. New Certificateless Generalized Signcryption Scheme for Internet of Things Environment [J]. Computer Science, 2022, 49(3): 329-337.
[10] LI Bei-bei, SONG Jia-rui, DU Qing-yun, HE Jun-jiang. DRL-IDS:Deep Reinforcement Learning Based Intrusion Detection System for Industrial Internet of Things [J]. Computer Science, 2021, 48(7): 47-54.
[11] LI Jia-ming, ZHAO Kuo, QU Ting, LIU Xiao-xiang. Research and Analysis of Blockchain Internet of Things Based on Knowledge Graph [J]. Computer Science, 2021, 48(6A): 563-567.
[12] XIANG Chang-sheng, CHEN Zhi-gang. Chaotic Prediction Model of Network Traffic for Massive Data [J]. Computer Science, 2021, 48(5): 289-293.
[13] WANG Xi-long, LI Xin, QIN Xiao-lin. Collaborative Scheduling of Source-Grid-Load-Storage with Distributed State Awareness UnderPower Internet of Things [J]. Computer Science, 2021, 48(2): 23-32.
[14] WANG Wei-hong, CHEN Zhen-yu. Intelligent Manufacturing Security Model Based on Improved Blockchain [J]. Computer Science, 2021, 48(2): 295-302.
[15] LIU Xin, HUANG Yuan-yuan, LIU Zi-ang, ZHOU Rui. IoTGuardEye:A Web Attack Detection Method for IoT Services [J]. Computer Science, 2021, 48(2): 324-329.
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
No Suggested Reading articles found!