Zone-Based Indoor Localization Using Neural Networks: A View from a Real Testbed

Precise indoor localization is of great importance to automatically track people or objects indoors and plays a vital role in modern life. Despite a number of innovative research present in the literature indoor localization still remains an open problem. To trace the main reason we identify that in the present literature the tendency is to pinpoint the exact coordinates of a target device although most of the location based services (LBSs) do not require exact coordinates. To support LBS, one can simply divide the area of interest into several zones and perform ``zone-fencing'', i.e., find under which zone the user is currently located at. In this paper, we propose a zone-based indoor localization scheme using neural networks. With the results from real world indoor settings, we show that a number of empty clusters is generated when the traditional counter propagation network (CPN) is applied as is. But a slight modification to the CPN reduces the number of empty clusters significantly and provides promising accuracy. The proposed scheme outperforms ``k-Nearest Neighbor algorithm" (k-NN) and its promising accuracy makes it suitable for real-world deployment.

[1]  Rajen B. Bhatt,et al.  Improving the Accuracy of Fuzzy Decision Tree by Direct Back Propagation with Adaptive Learning Rate and Momentum Factor for User Localization , 2016 .

[2]  Jason Jianjun Gu,et al.  Deep Neural Networks for wireless localization in indoor and outdoor environments , 2016, Neurocomputing.

[3]  Guillermo Licea,et al.  Estimating Indoor Zone-Level Location Using Wi-Fi RSSI Fingerprinting Based on Fuzzy Inference System , 2013, 2013 International Conference on Mechatronics, Electronics and Automotive Engineering.

[4]  W. H. Engelmann,et al.  The National Human Activity Pattern Survey (NHAPS): a resource for assessing exposure to environmental pollutants , 2001, Journal of Exposure Analysis and Environmental Epidemiology.

[5]  Luca Benini,et al.  Bluetooth indoor localization with multiple neural networks , 2010, IEEE 5th International Symposium on Wireless Pervasive Computing 2010.

[6]  Hien Nguyen Van,et al.  Indoor Localization Using Multiple Wireless Technologies , 2007, 2007 IEEE Internatonal Conference on Mobile Adhoc and Sensor Systems.

[7]  Philipp Bolliger,et al.  Redpin - adaptive, zero-configuration indoor localization through user collaboration , 2008, MELT '08.

[8]  Tanuja Pande,et al.  A Blind Navigation System Using RFID for Indoor Environments , 2015 .

[9]  D. Larose k‐Nearest Neighbor Algorithm , 2005 .

[10]  Shih-Hau Fang,et al.  Indoor Location System Based on Discriminant-Adaptive Neural Network in IEEE 802.11 Environments , 2008, IEEE Transactions on Neural Networks.

[11]  Nan Li,et al.  A Wi-Fi Indoor Localization Strategy Using Particle Swarm Optimization Based Artificial Neural Networks , 2016, Int. J. Distributed Sens. Networks.

[12]  Jing Liu,et al.  Survey of Wireless Indoor Positioning Techniques and Systems , 2007, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews).

[13]  Yunhao Liu,et al.  LANDMARC: Indoor Location Sensing Using Active RFID , 2004, Proceedings of the First IEEE International Conference on Pervasive Computing and Communications, 2003. (PerCom 2003)..