DelFin: A Deep Learning Based CSI Fingerprinting Indoor Localization in IoT Context

Many applications in Internet of Things (IoT) require an ubiquitous localization to provide their services. Whereas the global navigation satellite systems are mainly used in outdoor environment, multiple solutions based on mobile sensors or wireless communication infrastructures exist for indoor localization. One of them is the fingerprinting approach which consists in collecting the signals at known locations in a studied area and estimating the locations of new incoming signals thanks to the collected database. This approach interests many researches due to its connection with machine learning concepts. In this paper we propose to implement a deep learning architecture for a fingerprinting localization based on Wi-Fi channel frequency responses in IoT context. Our solution, DelFin reduces the median and 90-th percentile localization errors up to 50% and 47% respectively compared to other fingerprinting methods. DelFin has been tested with different spatial distributions of training locations in the studied area and still performed the best results.

[1]  Mo Li,et al.  Precise Power Delay Profiling with Commodity Wi-Fi , 2015, IEEE Transactions on Mobile Computing.

[2]  Sepp Hochreiter,et al.  Self-Normalizing Neural Networks , 2017, NIPS.

[3]  Seth J. Teller,et al.  Growing an organic indoor location system , 2010, MobiSys '10.

[4]  Andrew Zisserman,et al.  Very Deep Convolutional Networks for Large-Scale Image Recognition , 2014, ICLR.

[5]  Hao Yu,et al.  Indoor positioning by distributed machine-learning based data analytics on smart gateway network , 2015, 2015 International Conference on Indoor Positioning and Indoor Navigation (IPIN).

[6]  Robert Jenssen,et al.  Kernel Entropy Component Analysis , 2010, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[7]  Moustafa Youssef,et al.  The Horus WLAN location determination system , 2005, MobiSys '05.

[8]  Kaishun Wu,et al.  FIFS: Fine-Grained Indoor Fingerprinting System , 2012, 2012 21st International Conference on Computer Communications and Networks (ICCCN).

[9]  Chen Liang,et al.  Fine-Grained Indoor Localization Using Single Access Point With Multiple Antennas , 2015, IEEE Sensors Journal.

[10]  Christoforos Panayiotou,et al.  Localization Using Radial Basis Function Networks and Signal Strength Fingerprints in WLAN , 2009, GLOBECOM 2009 - 2009 IEEE Global Telecommunications Conference.

[11]  Jimmy Ba,et al.  Adam: A Method for Stochastic Optimization , 2014, ICLR.

[12]  Naser Al-Falahy,et al.  Technologies for 5G Networks: Challenges and Opportunities , 2017, IT Professional.

[13]  Fredrik Tufvesson,et al.  Deep convolutional neural networks for massive MIMO fingerprint-based positioning , 2017, 2017 IEEE 28th Annual International Symposium on Personal, Indoor, and Mobile Radio Communications (PIMRC).

[14]  Nirwan Ansari,et al.  Localization by Fusing a Group of Fingerprints via Multiple Antennas in Indoor Environment , 2016, IEEE Transactions on Vehicular Technology.

[15]  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).

[16]  Shih-Hau Fang,et al.  Location Fingerprinting In A Decorrelated Space , 2008, IEEE Transactions on Knowledge and Data Engineering.

[17]  P. Stoica,et al.  Novel eigenanalysis method for direction estimation , 1990 .

[18]  Hao Chen,et al.  ConFi: Convolutional Neural Networks Based Indoor Wi-Fi Localization Using Channel State Information , 2017, IEEE Access.

[19]  Abdo Gaber,et al.  A Study of Wireless Indoor Positioning Based on Joint TDOA and DOA Estimation Using 2-D Matrix Pencil Algorithms and IEEE 802.11ac , 2015, IEEE Transactions on Wireless Communications.

[20]  Sachin Katti,et al.  SpotFi: Decimeter Level Localization Using WiFi , 2015, SIGCOMM.

[21]  David Wetherall,et al.  Predictable 802.11 packet delivery from wireless channel measurements , 2010, SIGCOMM '10.

[22]  J.-M. Conrat,et al.  A Multibands Wideband Propagation Channel Sounder from 2 to 60 GHz , 2006, 2006 IEEE Instrumentation and Measurement Technology Conference Proceedings.

[23]  Mohammad Azzeh,et al.  User Movement Prediction: The Contribution of Machine Learning Techniques , 2016, 2016 15th IEEE International Conference on Machine Learning and Applications (ICMLA).

[24]  R. O. Schmidt,et al.  Multiple emitter location and signal Parameter estimation , 1986 .

[25]  Shiwen Mao,et al.  BiLoc: Bi-Modal Deep Learning for Indoor Localization With Commodity 5GHz WiFi , 2017, IEEE Access.

[26]  Swarun Kumar,et al.  Decimeter-Level Localization with a Single WiFi Access Point , 2016, NSDI.