Geomagnetism-Aided Indoor Wi-Fi Radio-Map Construction via Smartphone Crowdsourcing

Wi-Fi radio-map construction is an important phase in indoor fingerprint localization systems. Traditional methods for Wi-Fi radio-map construction have the problems of being time-consuming and labor-intensive. In this paper, an indoor Wi-Fi radio-map construction method is proposed which utilizes crowdsourcing data contributed by smartphone users. We draw indoor pathway map and construct Wi-Fi radio-map without requiring manual site survey, exact floor layout and extra infrastructure support. The key novelty is that it recognizes road segments from crowdsourcing traces by a cluster based on magnetism sequence similarity and constructs an indoor pathway map with Wi-Fi signal strengths annotated on. Through experiments in real world indoor areas, the method is proved to have good performance on magnetism similarity calculation, road segment clustering and pathway map construction. The Wi-Fi radio maps constructed by crowdsourcing data are validated to provide competitive indoor localization accuracy.

[1]  Qun Li,et al.  Scalable Indoor Localization via Mobile Crowdsourcing and Gaussian Process , 2016, Sensors.

[2]  Hong Yuan,et al.  Smartphone-based integrated PDR/GPS/Bluetooth pedestrian location , 2017 .

[3]  Jianxin Wu,et al.  GROPING: Geomagnetism and cROwdsensing Powered Indoor NaviGation , 2015, IEEE Transactions on Mobile Computing.

[4]  Jonathan Ledlie,et al.  Mole: A scalable, user-generated WiFi positioning engine , 2011, 2011 International Conference on Indoor Positioning and Indoor Navigation.

[5]  Ismail Güvenç,et al.  A Survey on TOA Based Wireless Localization and NLOS Mitigation Techniques , 2009, IEEE Communications Surveys & Tutorials.

[6]  Yinfeng Wu,et al.  A Radio-Map Automatic Construction Algorithm Based on Crowdsourcing , 2016, Sensors.

[7]  Valérie Renaudin,et al.  Magnetic, Acceleration Fields and Gyroscope Quaternion (MAGYQ)-Based Attitude Estimation with Smartphone Sensors for Indoor Pedestrian Navigation , 2014, Sensors.

[8]  Shueng-Han Gary Chan,et al.  Wi-Fi Fingerprint-Based Indoor Positioning: Recent Advances and Comparisons , 2016, IEEE Communications Surveys & Tutorials.

[9]  Di Wu,et al.  Heading Estimation for Indoor Pedestrian Navigation Using a Smartphone in the Pocket , 2015, Sensors.

[10]  Li Li,et al.  HIWL: An Unsupervised Learning Algorithm for Indoor Wireless Localization , 2013, 2013 12th IEEE International Conference on Trust, Security and Privacy in Computing and Communications.

[11]  Rong Zheng,et al.  UMLI: An unsupervised mobile locations extraction approach with incomplete data , 2013, 2013 IEEE Wireless Communications and Networking Conference (WCNC).

[12]  Konstantinos N. Plataniotis,et al.  Kernel-Based Positioning in Wireless Local Area Networks , 2007, IEEE Transactions on Mobile Computing.

[13]  Laurence T. Yang,et al.  Indoor smartphone localization via fingerprint crowdsourcing: challenges and approaches , 2016, IEEE Wireless Communications.

[14]  Mun Choon Chan,et al.  PiLoc: A self-calibrating participatory indoor localization system , 2014, IPSN-14 Proceedings of the 13th International Symposium on Information Processing in Sensor Networks.

[15]  Sumit Mittal,et al.  KARMA: Improving WiFi-based indoor localization with dynamic causality calibration , 2014, 2014 Eleventh Annual IEEE International Conference on Sensing, Communication, and Networking (SECON).

[16]  Yunhao Liu,et al.  WILL: Wireless indoor localization without site survey , 2012, 2012 Proceedings IEEE INFOCOM.

[17]  Seth J. Teller,et al.  Growing an organic indoor location system , 2010, MobiSys '10.

[18]  Juha Röning,et al.  Magnetic field SLAM exploration: Frequency domain Gaussian processes and informative route planning , 2015, 2015 European Conference on Mobile Robots (ECMR).

[19]  Henry Tirri,et al.  A Probabilistic Approach to WLAN User Location Estimation , 2002, Int. J. Wirel. Inf. Networks.

[20]  Anshul Rai,et al.  Zee: zero-effort crowdsourcing for indoor localization , 2012, Mobicom '12.

[21]  Ramón F. Brena,et al.  Magnetic Field Feature Extraction and Selection for Indoor Location Estimation , 2014, Sensors.

[22]  Yan Zhou,et al.  Activity Recognition and Semantic Description for Indoor Mobile Localization , 2017, Sensors.

[23]  Wei Tu,et al.  A Robust Crowdsourcing-Based Indoor Localization System , 2017, Sensors.

[24]  Alok Aggarwal,et al.  Efficient, generalized indoor WiFi GraphSLAM , 2011, 2011 IEEE International Conference on Robotics and Automation.

[25]  Philipp Bolliger,et al.  Redpin - adaptive, zero-configuration indoor localization through user collaboration , 2008, MELT '08.

[26]  Hansung Lee,et al.  Estimation of Heading Angle Difference Between User and Smartphone Utilizing Gravitational Acceleration Extraction , 2016, IEEE Sensors Journal.

[27]  Peilin Liu,et al.  Vector Graph Assisted Pedestrian Dead Reckoning Using an Unconstrained Smartphone , 2015, Sensors.

[28]  Mohammed Khider,et al.  Simultaneous Localization and Mapping for pedestrians using distortions of the local magnetic field intensity in large indoor environments , 2013, International Conference on Indoor Positioning and Indoor Navigation.

[29]  Neil D. Lawrence,et al.  WiFi-SLAM Using Gaussian Process Latent Variable Models , 2007, IJCAI.

[30]  Yi Lu,et al.  A Context-Recognition-Aided PDR Localization Method Based on the Hidden Markov Model , 2016, Sensors.

[31]  Haiyong Luo,et al.  An indoor self-localization algorithm using the calibration of the online magnetic fingerprints and indoor landmarks , 2016, 2016 International Conference on Indoor Positioning and Indoor Navigation (IPIN).

[32]  Widyawan,et al.  Smartphone-based Pedestrian Dead Reckoning as an indoor positioning system , 2012, 2012 International Conference on System Engineering and Technology (ICSET).

[33]  Hong Yuan,et al.  A novel method of WiFi fingerprint positioning using spatial multi-points matching , 2016, 2016 International Conference on Indoor Positioning and Indoor Navigation (IPIN).