Wireless Positioning as a Cloud Based Service

In the recent time seamless positioning in hybrid environment is becoming hot topic for large number of researchers. In this paper we will present approach for seamless positioning using modular positioning system implemented as a cloud service. The idea of the proposed system is to provide position estimates in both indoor and outdoor environments by using different positioning modules. Currently we have implemented three positioning modules – GPS module, Wi-Fi module and GSM module. Each of the modules have different role in the system. GPS should provide positioning information mainly in outdoor environment, Wi-Fi module should be used mainly in indoor environment and GSM module should provide position estimates in case that GPS and Wi-Fi positioning modules are not able to estimate position of the device. For example due to low number of received signals or poor quality of signals. In the proposed system positioning GSM and Wi-Fi modules utilize fingerprinting approach for the position estimation.

[1]  Andrew G. Dempster,et al.  Indoor Positioning Techniques Based on Wireless LAN , 2007 .

[2]  Jiyun Shen,et al.  Direction estimation for cellular enhanced cell-ID positioning using multiple sector observations , 2010, 2010 International Conference on Indoor Positioning and Indoor Navigation.

[3]  Nirvana Meratnia,et al.  Using time-of-flight for WLAN localization: feasibility study , 2006 .

[4]  Sudarshan S. Chawathe.  Low-latency indoor localization using bluetooth beacons , 2009, 2009 12th International IEEE Conference on Intelligent Transportation Systems.

[5]  Eyal de Lara,et al.  GSM indoor localization , 2007, Pervasive Mob. Comput..

[6]  Geoffrey G. Messier,et al.  Using WLAN Infrastructure for Angle-of-Arrival Indoor User Location , 2008, 2008 IEEE 68th Vehicular Technology Conference.

[7]  Peter Brida,et al.  A Modular Localization System as a Positioning Service for Road Transport , 2014, Sensors.

[8]  M. Penhaker,et al.  Wireless Body Sensor Network in Health Maintenance Systems , 2011 .

[9]  Peter Brida,et al.  Performance Comparison of Similarity Measurements for Database Correlation Localization Method , 2011, ACIIDS.

[10]  Fredrik Gustafsson,et al.  Mobile Positioning Using Wireless Networks , 2005 .

[11]  Malcolm David Macnaughtan,et al.  Positioning GSM telephones , 1998, IEEE Commun. Mag..

[12]  Santiago Mazuelas,et al.  Robust Indoor Positioning Provided by Real-Time RSSI Values in Unmodified WLAN Networks , 2009, IEEE Journal of Selected Topics in Signal Processing.

[13]  Ondrej Krejcar,et al.  Modern smart device-based concept of sensoric networks , 2013, EURASIP J. Wirel. Commun. Netw..

[14]  Kamalika Chaudhuri,et al.  Location determination of a mobile device using IEEE 802.11b access point signals , 2003, 2003 IEEE Wireless Communications and Networking, 2003. WCNC 2003..

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

[16]  Ján Papaj,et al.  Security and QoS Integration Model for MANETs , 2012, Comput. Informatics.

[17]  F. Gustafsson,et al.  Mobile positioning using wireless networks: possibilities and fundamental limitations based on available wireless network measurements , 2005, IEEE Signal Processing Magazine.

[18]  Stuart A. Golden,et al.  Sensor Measurements for Wi-Fi Location with Emphasis on Time-of-Arrival Ranging , 2007, IEEE Transactions on Mobile Computing.

[19]  Lu Han,et al.  Intelligent Alarm Positioning System Based on Zigbee Wireless Networks , 2013, 2013 6th International Conference on Intelligent Networks and Intelligent Systems.

[20]  P. Brida,et al.  Impact of Wi-Fi Access Points on performance of RBF localization algorithm , 2012, 2012 ELEKTRO.

[21]  Christian Hoene,et al.  Measuring Round Trip Times to Determine the Distance Between WLAN Nodes , 2005, NETWORKING.