A Visible Light Based Indoor Positioning System

In this paper, we propose a novel indoor localization scheme, Lightitude, by exploiting ubiquitous visible lights, which are necessarily and densely deployed in almost all indoor environments. We unveil two phenomena of lights available for positioning: 1) the light strength varies according to different light sources, which can be easily detected by light sensors embedded in COTS devices (e.g., smart-phone, smart-glass and smart-watch); 2) the light strength is stable in different times of the day thus exploiting it can avoid frequent site-survey and database maintenance. Hence, a user could locate oneself by differentiating the light source of received light strength (RLS). However, different from existing positioning systems that exploit special LEDs, ubiquitous visible lights lack fingerprints that can uniquely identify the light source, which results in an ambiguity problem that an RLS may correspond to multiple positions. Moreover, RLS is not only determined by device’s position, but also seriously affected by its orientation, which causes great complexity in site-survey. To address these challenges, we first propose and validate a realistic light strength model that can attributes RLS to arbitrary positions with heterogenous orientations. This model is further perfected by taking account of the device diversity, influence of multiple light sources and shading of obstacles. Then we design a localizing scheme that harness user’s mobility to generate spatial-related RLS to tackle the position-ambiguity problem of a single RLS, which is robust against sunlight interference, shading effect of human-body and unpredictable behaviours (e.g., put the device in pocket) of user. Experiment results show that Lightitude achieves mean accuracy 1.93m and 1.98m in office (720m2) and library scenario (960m2) respectively.

[1]  L. Iftode,et al.  FiatLux : Fingerprinting Rooms Using Light Intensity , 2022 .

[2]  Fadel Adib,et al.  See through walls with WiFi! , 2013, SIGCOMM.

[3]  Yin Chen,et al.  FM-based indoor localization , 2012, MobiSys '12.

[4]  Guobin Shen,et al.  Epsilon: A Visible Light Based Positioning System , 2014, NSDI.

[5]  Mo Li,et al.  Travi-Navi: self-deployable indoor navigation system , 2014, MobiCom.

[6]  Sachin Katti,et al.  PinPoint: Localizing Interfering Radios , 2013, NSDI.

[7]  Anthony Rowe,et al.  Visual light landmarks for mobile devices , 2014, IPSN-14 Proceedings of the 13th International Symposium on Information Processing in Sensor Networks.

[8]  Moustafa Youssef,et al.  The Horus WLAN location determination system , 2005, MobiSys '05.

[9]  F. Seco,et al.  A comparison of Pedestrian Dead-Reckoning algorithms using a low-cost MEMS IMU , 2009, 2009 IEEE International Symposium on Intelligent Signal Processing.

[10]  Jie Yang,et al.  Push the limit of WiFi based localization for smartphones , 2012, Mobicom '12.

[11]  Eamonn Keogh Exact Indexing of Dynamic Time Warping , 2002, VLDB.

[12]  Yunhao Liu,et al.  Shake and walk: Acoustic direction finding and fine-grained indoor localization using smartphones , 2014, IEEE INFOCOM 2014 - IEEE Conference on Computer Communications.

[13]  Guobin Shen,et al.  BeepBeep: a high accuracy acoustic ranging system using COTS mobile devices , 2007, SenSys '07.

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

[15]  Guobin Shen,et al.  Walkie-Markie: Indoor Pathway Mapping Made Easy , 2013, NSDI.

[16]  Jue Wang,et al.  Dude, where's my card?: RFID positioning that works with multipath and non-line of sight , 2013, SIGCOMM.

[17]  Prabal Dutta,et al.  Luxapose: indoor positioning with mobile phones and visible light , 2014, MobiCom.

[18]  Rob Miller,et al.  3D Tracking via Body Radio Reflections , 2014, NSDI.

[19]  Hideo Makino,et al.  Basic study on indoor location estimation using Visible Light Communication platform , 2008, 2008 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[20]  Moustafa Youssef,et al.  No need to war-drive: unsupervised indoor localization , 2012, MobiSys '12.

[21]  Oliver Amft,et al.  LuxTrace: indoor positioning using building illumination , 2007, Personal and Ubiquitous Computing.

[22]  Yunhao Liu,et al.  Locating in fingerprint space: wireless indoor localization with little human intervention , 2012, Mobicom '12.

[23]  S. Haruyama,et al.  High-accuracy positioning system using visible LED lights and image sensor , 2008, 2008 IEEE Radio and Wireless Symposium.