A Study on Room-Level Accuracy of Wi-Fi Fingerprinting-Based Indoor Localization Systems

Yıl 2019, , 17 - 22, 22.03.2019

Umut Can Çabuk , Feriştah Dalkılıç , Orhan Dağdeviren

https://doi.org/10.18466/cbayarfbe.416970

Cited By: 1

Öz

Global positioning system and other outdoor positioning mechanisms are
already subject to comprehensive research and development for almost half a
century. Conversely, indoor positioning services became a hot topic in the last
decade. Since GPS (and. other outdoor solutions) do not work reliably in most
indoor environments, researchers and developers are working on accurate
positioning solutions, especially tailored for indoor places. However; due to
walls, furniture, people and other obstacles, absolute location estimation is
very hard and expensive to achieve in indoor places. In addition, accuracy
needs depend on the scenario and application. In this study, we have studied the
feasibility of room-level location detection in home and office environments.
We have focused on examining the quality of room-wise detection accuracy of the
fingerprinting method that is applied along with standard Wi-Fi radio
infrastructure. We have conducted experiments in a multi-storey office building
made of concrete and aerated concrete bricks with many rooms, in which it is
significantly hard to accurately estimate the correct place of a thing, using
radio signals. To the best of our knowledge, our paper is the first study that
investigates the room-level accuracy of Wi-Fi fingerprinting-based indoor
localization systems. We have found out that, it is possible to feasibly
achieve room-level detection with good accuracy, via a pre-calculated room-specific
received signal strength indicator threshold value.

Anahtar Kelimeler

Indoor Positioning, Localization, Wi-Fi, Fingerprinting, Received Signal Strength Indicator

Kaynakça

• 1. Kim, S, Ha, S, Saad, A, Kim, J. Indoor Positioning System Techniques and Security, in proc. of the IEEE Fourth International Conference on e-Technologies and Networks for Development (ICeND), Lodz, Poland, 2015, pp 1-4.
• 2. Koyuncu, H, Yang, S.H, A survey of indoor positioning and object locating system, International Journal of Computer Science and Network Security, 2010, 10(5), 121-128.
• 3. Li, B, Salter, J, Dempster, A.G, Rizos, C. Indoor Positioning Techniques Based on Wireless LAN, proceedings of the IEEE International Conference on Wireless Broadband and Ultra-Wideband Communications (AusWireless), Sydney, Australia, 2006.
• 4. Seco, F, Plagemann, C, Jiménez, A.R, Burgard, W. Improving RFID-Based Indoor Positioning Accuracy Using Gaussian Processes, proceedings of the IEEE Int. Conference on Indoor Positioning and Indoor Navigation (IPIN), Zurich, Switzerland, 2010, pp 1-8.
• 5. Mazuelas, S, Bahillo, A, Lorenzo, R.M, Fernandez, P, Lago, F.A, Garcia, E, Blas, J, Abril, E.J, Robust indoor positioning provided by real-time RSSI values in unmodified WLAN networks, IEEE Journal of selected topics in signal processing, 2009, 3(5), 821-831. doi:10.1109/JSTSP.2009.2029191.
• 6. Jekabsons, G, Kairish, V, Zuravlyov, V, An analysis of Wi-Fi based indoor positioning accuracy, Scientific Journal of Riga Technical University, Computer Sciences, 2011, 44(1), 131-137. doi:10.2478/v10143-011-0031-4.
• 7. Aruba Networks Inc., Indoor 802.11n site survey and planning. https://community.arubanetworks.com/aruba/attachments/aruba/unified-wired-wireless-access/588/1/indoor80211n_2012-05-31.pdf, 2012 (accessed 12.04.2018).
• 8. Internal Positioning, Framework for internal navigation and discovery (FIND). https://www.internalpositioning.com/faq/, 2017 (accessed 12.04.2018)
• 9. Group, H.L, Hand washing: A modest measure—with big effects, BMJ: British Medical Journal, 1999, 318(7185), 686.
• 10. Whitby, M, McLaws, M. L, Ross, M.W, Why healthcare workers don't wash their hands: a behavioral explanation, Infection Control & Hospital Epidemiology, 2006, 27(5), 484-492. doi:10.1086/503335.
• 11. Chapre, Y, Ignjatovic, A, Seneviratne, A, Jha, S. Csi-mimo: Indoor Wi-Fi Fingerprinting System., in proc. of the IEEE 39th Conf. on Local Comp. Networks (LCN), Edmonton, AB, Canada, 2014, pp 202-209.