Dynamic Wi-Fi RSSI normalization in unmapped locations

With the growing availability of open access WLAN networks, we assisted to the increase of marketing services that are based on the data collected from the WLAN access points. The identification of visitors of a commercial venue using WLAN data is one of the issues to create successful marketing products. One of the ways to separate visitors is to analyse the RSSI of the mobile devices signals coming to various access points at the venue. Nevertheless, the indoor signal distortion makes RSSI based methods unreliable. In this work we propose the algorithm for the WLAN based RSSI normalization in uncontrolled environments. Our approach is based on the two steps, where at first based on the collected data we detect the devices whose RSSI can be taken as a basic one. At the second step the algorithm allows based on the previously detected basic RSSI to normalize the received signal from mobile devices.We provide the analysis of a real dataset ofWLAN probes collected in several real commercial venues in Italy.

[1]  Elena Simona Lohan,et al.  Deconvolution-based indoor localization with WLAN signals and unknown access point locations , 2013, 2013 International Conference on Localization and GNSS (ICL-GNSS).

[2]  Peter Brida,et al.  Rank based fingerprinting algorithm for indoor positioning , 2011, 2011 International Conference on Indoor Positioning and Indoor Navigation.

[3]  Kin K. Leung,et al.  A Survey of Indoor Localization Systems and Technologies , 2017, IEEE Communications Surveys & Tutorials.

[4]  Markku Renfors,et al.  Distance-Based Interpolation and Extrapolation Methods for RSS-Based Localization With Indoor Wireless Signals , 2015, IEEE Transactions on Vehicular Technology.

[5]  Tao Chen,et al.  From one to crowd: a survey on crowdsourcing-based wireless indoor localization , 2018, Frontiers of Computer Science.

[6]  Behnam Dezfouli,et al.  Empirical analysis and modeling of Bluetooth low-energy (BLE) advertisement channels , 2018, 2018 17th Annual Mediterranean Ad Hoc Networking Workshop (Med-Hoc-Net).

[7]  M. Watheq El-Kharashi,et al.  Secure WiFi Fingerprinting-based Localization , 2018, 2018 13th International Conference on Computer Engineering and Systems (ICCES).

[8]  Henri Nurminen,et al.  A Survey on Wireless Transmitter Localization Using Signal Strength Measurements , 2017, Wirel. Commun. Mob. Comput..

[9]  Adriano J. C. Moreira,et al.  Combining similarity functions and majority rules for multi-building, multi-floor, WiFi positioning , 2012, 2012 International Conference on Indoor Positioning and Indoor Navigation (IPIN).

[10]  Robert Harle,et al.  Location Fingerprinting With Bluetooth Low Energy Beacons , 2015, IEEE Journal on Selected Areas in Communications.

[11]  Shuang-Hua Yang,et al.  A Survey of Indoor Positioning and Object Locating Systems , 2010 .

[12]  Simo Ali-Löytty,et al.  A comparative survey of WLAN location fingerprinting methods , 2009, 2009 6th Workshop on Positioning, Navigation and Communication.