AutLoc: Deep Autoencoder for Indoor Localization with RSS Fingerprinting

Wi-Fi based indoor localization has attracted great interest due to its ubiquitous access in many indoor environ- ments. However, the accuracy is deteriorated by the complex indoor propagation environments, which result in variable received signal strength (RSS). In this paper, we propose to utilize an autoencoder to improve the accuracy of indoor localization by preprocessing the noisy RSS. The AutLoc system includes an offline training phase and an online localization phase. In the offline training phase, we train the deep autoencoder to denoise the measured data and then build the RSS fingerprints according to the trained weights. In the online localization phase, we adopt three machine learning algorithms, which are random forest regression, multi-player perceptron classification and multi-player perceptron regression, to estimate the location. Averaging over the results of three algorithms, we then obtain the final estimated location. Simulation results justify superiority of the proposed AutLoc system over previous indoor location schemes in vast scenarios.

[1]  Shueng-Han Gary Chan,et al.  Wi-Fi Fingerprint-Based Indoor Positioning: Recent Advances and Comparisons , 2016, IEEE Communications Surveys & Tutorials.

[2]  Zhenzhong Chen,et al.  3-D BLE Indoor Localization Based on Denoising Autoencoder , 2017, IEEE Access.

[3]  Xiao Zhang,et al.  Device-Free Wireless Localization and Activity Recognition: A Deep Learning Approach , 2017, IEEE Transactions on Vehicular Technology.

[4]  Sanghyuk Lee,et al.  Large-scale location-aware services in access: Hierarchical building/floor classification and location estimation using Wi-Fi fingerprinting based on deep neural networks , 2017 .

[5]  A. V. Olgac,et al.  Performance Analysis of Various Activation Functions in Generalized MLP Architectures of Neural Networks , 2011 .

[6]  Jinbao Zhang,et al.  Overview of received signal strength based fingerprinting localization in indoor wireless LAN environments , 2013, 2013 5th IEEE International Symposium on Microwave, Antenna, Propagation and EMC Technologies for Wireless Communications.

[7]  Maxim Shchekotov,et al.  Indoor Localization Method Based on Wi-Fi Trilateration Technique , 2014 .

[8]  Xinbing Wang,et al.  Performance Analysis of RSS Fingerprinting Based Indoor Localization , 2017, IEEE Transactions on Mobile Computing.

[9]  Prashant Krishnamurthy,et al.  Analysis of WLAN's received signal strength indication for indoor location fingerprinting , 2012, Pervasive Mob. Comput..

[10]  Prashant Krishnamurthy,et al.  Properties of indoor received signal strength for WLAN location fingerprinting , 2004, The First Annual International Conference on Mobile and Ubiquitous Systems: Networking and Services, 2004. MOBIQUITOUS 2004..

[11]  Mohamed Khedr,et al.  An enhanced WiFi indoor localization system based on machine learning , 2016, 2016 International Conference on Indoor Positioning and Indoor Navigation (IPIN).

[12]  Paramvir Bahl,et al.  RADAR: an in-building RF-based user location and tracking system , 2000, Proceedings IEEE INFOCOM 2000. Conference on Computer Communications. Nineteenth Annual Joint Conference of the IEEE Computer and Communications Societies (Cat. No.00CH37064).

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

[14]  Gaël Varoquaux,et al.  Scikit-learn: Machine Learning in Python , 2011, J. Mach. Learn. Res..

[15]  David Akopian,et al.  Modern WLAN Fingerprinting Indoor Positioning Methods and Deployment Challenges , 2016, IEEE Communications Surveys & Tutorials.

[16]  Seyed Ali Ghorashi,et al.  A Fingerprint Method for Indoor Localization Using Autoencoder Based Deep Extreme Learning Machine , 2018, IEEE Sensors Letters.

[17]  Sinem Bozkurt Keser,et al.  Integration of classification algorithms for indoor positioning system , 2017, 2017 International Conference on Computer Science and Engineering (UBMK).

[18]  David H. Wolpert,et al.  No free lunch theorems for optimization , 1997, IEEE Trans. Evol. Comput..

[19]  Yibo Yu An Efficient Wi-Fi RSS Indoor Positioning System and Its Client-server Implementation , 2013 .

[20]  Serkan Günal,et al.  A comparative study on machine learning algorithms for indoor positioning , 2015, 2015 International Symposium on Innovations in Intelligent SysTems and Applications (INISTA).

[21]  Sanghyuk Lee,et al.  A scalable deep neural network architecture for multi-building and multi-floor indoor localization based on Wi-Fi fingerprinting , 2017, ArXiv.