Modified WiFi-RSS Fingerprint Technique to locate Indoors-Smartphones: FENG building at Koya University as a case study

Halgurd S. Maghdid; Ladeh Sardar Abdulrahman; Mohammed H. Ahmed; Azhin Tahir Sabir

doi:10.24017/science.2017.3.41

Authors

Halgurd S. Maghdid Department of Software Engineering ,Koya University, Koya, Sulaimani, Iraq
Ladeh Sardar Abdulrahman Department of Software Engineering , ,Koya University, Koya ,Sulaimani, Iraq
Mohammed H. Ahmed Department of Computer Science, University of Raparin Ranyah ,Sulaimani, Iraq
Azhin Tahir Sabir Department of Software Engineering , Koya University, Koya ,Sulaimani, Iraq

Abstract

Positioning system used for different purposes and different services, many researches are going on to find a more accurate position with low error within high performance. There are many localization solutions with different architectures, configurations, accuracies and reliabilities for both outdoors and indoors. For example, Global Navigation Satellite System (GNSS) technology has been used for outdoors. Global Positioning System (GPS) is one of the most common outdoors tracking solutions in the world, for outdoors, however, when indoors; it could not be accurately tracked users by using a GPS system. This is because, when users enters into indoors the GPS signals will no longer available due to blocked by the roof of buildings and it is no longer considered as a viable option. WiFi Positioning System (WPS) can be used as an alternative solution to define users’ position, especially when GPS signal is not available. Further, WPS is a low cost solution, because there is no need to deploying WiFi Access Points (WAPs) in the vicinity, as they are installed to access the Internet. In this paper, specifically, WiFi-RSS Fingerprinting technique is used to locate smartphones using WAPs signals with a modified calculation. The new modified calculation is to dynamic weighting of the WAPs RSS values based on the real-live indoors structure. The achieved positioning accuracy, based on several trial experiments, is up to 6 meters via the implemented algorithm in the MALTAB.

Keywords:

Mobile Computing; WiFi; RSS-Fingerprinting; k-NN; Indoor positioning; Android Smartphones.

References

[1] L. Vladimir, M. Nikola, T. Milan, and others, Location-Based Applications for Smartphones., 2015.
[2] J. Ryoo, H. Kim, and S.R. Das, "Geo-fencing: Geographical-fencing based energy-aware proactive framework for mobile devices," in Quality of Service (IWQoS), 2012 IEEE 20th International Workshop on, june 2012, pp. 1-9.
https://doi.org/10.1109/IWQoS.2012.6245993
[3] D. Park and J. Goo Park, "An enhanced ranging scheme using WiFi RSSI measurements for ubiquitous location," in Computers, Networks, Systems and Industrial Engineering (CNSI), 2011 First ACIS/JNU International Conference on.: IEEE, 2011.
https://doi.org/10.1109/CNSI.2011.29
[4] D. Lymberopoulos et al., "A realistic evaluation and comparison of indoor location technologies: Experiences and lessons learned," in Proceedings of the 14th international conference on information processing in sensor networks.: ACM, 2015.
https://doi.org/10.1145/2737095.2737726
[5] Y. Zhao, K. Liu, Yongtao Ma, and Zhuo Li, An improved k-NN algorithm for localization in multipath environments.: Springer, 2014, vol. 2014.
https://doi.org/10.1186/1687-1499-2014-208
[6] R. Mautz, "The challenges of indoor environments and specification on some alternative positioning systems," in Positioning, Navigation and Communication, 2009. WPNC 2009. 6th Workshop on.: IEEE, 2009, pp. 29--36.
https://doi.org/10.1109/WPNC.2009.4907800
[7] P. Jiang, Y. Zhang, Wenyan Fu, Huiyu Liu, and Xiaolin Su, Indoor mobile localization based on Wi-Fi fingerprint's important access point., 2015.
https://doi.org/10.1155/2015/429104
[8] X. Piao et al., "RSS Fingerprint Based Indoor Localization Using Sparse Representation with Spatio-Temporal Constraint," Sensors, vol. 16, p. 1845, 2016.
https://doi.org/10.3390/s16111845
[9] M. Quan, E. Navarro, and Benjamin Peuker, "Wi-fi localization using rssi fingerprinting," 2010.
[10] R. Mautz, Indoor positioning technologies.: ETH Zurich, Department of Civil, Environmental and Geomatic Engineering, Institute of Geodesy and Photogrammetry Zurich, 2012.