CRAFT reducing the effort for indoor localisation

Indoor localisation systems have slowly become more and more accurate. Each localisation system needs tuning to affect reasonable performance. In this paper we propose CRAFT, a crowd sourced approach to constructing a WiFi fingerprint database. The method uses a temporarily deployment of a small number of anchor nodes to roughly locate the position of the WiFi sample. Through thorough experiments in a real-world building, CRAFT's error is 2.2 m a decrease of 25% when compare to other published results.

[1]  Yunhao Liu,et al.  Smartphones Based Crowdsourcing for Indoor Localization , 2015, IEEE Transactions on Mobile Computing.

[2]  Shahrokh Valaee,et al.  Joint Indoor Localization and Radio Map Construction with Limited Deployment Load , 2015, IEEE Transactions on Mobile Computing.

[3]  Robert Harle,et al.  A Survey of Indoor Inertial Positioning Systems for Pedestrians , 2013, IEEE Communications Surveys & Tutorials.

[4]  Haibo Zhang,et al.  Emender: Signal filter for trilateration based indoor localisation , 2016, 2016 IEEE 27th Annual International Symposium on Personal, Indoor, and Mobile Radio Communications (PIMRC).

[5]  Igor Bisio,et al.  A Trainingless WiFi Fingerprint Positioning Approach Over Mobile Devices , 2014, IEEE Antennas and Wireless Propagation Letters.

[6]  Martin Werner,et al.  Indoor Location-Based Services , 2014, Springer International Publishing.

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

[8]  Christian Esposito,et al.  Calibrating Indoor Positioning Systems with Low Efforts , 2014, IEEE Transactions on Mobile Computing.

[9]  Laurence T. Yang,et al.  Indoor positioning via subarea fingerprinting and surface fitting with received signal strength , 2015, Pervasive Mob. Comput..

[10]  Subrata Goswami.  Indoor Location Technologies , 2012 .

[11]  Sangjae Lee,et al.  A crowdsourcing-based global indoor positioning and navigation system , 2016, Pervasive Mob. Comput..

[12]  Sana Salous,et al.  Radio Propagation Measurement and Channel Modelling , 2013 .

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

[14]  Hojung Cha,et al.  Crowdsensing-based Wi-Fi radio map management using a lightweight site survey , 2015, Comput. Commun..

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

[16]  Agathoniki Trigoni,et al.  Lightweight map matching for indoor localisation using conditional random fields , 2014, IPSN-14 Proceedings of the 13th International Symposium on Information Processing in Sensor Networks.

[17]  Maria Papadopouli,et al.  Low-dimensional signal-strength fingerprint-based positioning in wireless LANs , 2014, Ad Hoc Networks.

[18]  Noel E. O'Connor,et al.  SEAMLOC: Seamless Indoor Localization Based on Reduced Number of Calibration Points , 2014, IEEE Transactions on Mobile Computing.

[19]  Hojung Cha,et al.  A Participatory Service Platform for Indoor Location-Based Services , 2015, IEEE Pervasive Computing.

[20]  Henri Nurminen,et al.  Particle filter and smoother for indoor localization , 2013, International Conference on Indoor Positioning and Indoor Navigation.

[21]  Yang Dongkai,et al.  Flexible indoor localization and tracking system based on mobile phone , 2016 .

[22]  Paramvir Bahl,et al.  RADAR: an in-building RF-based user location and tracking system , 2000, Proceedings IEEE INFOCOM 2000. Conference on Computer Communications. Nineteenth Annual Joint Conference of the IEEE Computer and Communications Societies (Cat. No.00CH37064).