Deep Convolutional Neural Networks for Indoor Localization with CSI Images

With the increasing demand of location-based services, Wi-Fi based localization has attracted great interest because it provides ubiquitous access in indoor environments. In this paper, we propose CiFi, deep convolutional neural networks (DCNN) for indoor localization with commodity 5GHz WiFi. Leveraging a modified device driver, we extract phase data of channel state information (CSI), which is used to estimate the angle of arrival (AoA). We then create estimated AoA images as input to a DCNN, to train the weights in the offline phase. The location of mobile device is predicted based using the trained DCNN and new CSI AoA images. We implement the proposed CiFi system with commodity Wi-Fi devices in the 5GHz band and verify its performance with extensive experiments in two representative indoor environments.

[1]  Xiangyu Wang,et al.  CiFi: Deep convolutional neural networks for indoor localization with 5 GHz Wi-Fi , 2017, 2017 IEEE International Conference on Communications (ICC).

[2]  Jie Xiong,et al.  Phaser: enabling phased array signal processing on commodity WiFi access points , 2014, MobiCom.

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

[4]  Chao Yang,et al.  PhaseBeat: Exploiting CSI Phase Data for Vital Sign Monitoring with Commodity WiFi Devices , 2017, 2017 IEEE 37th International Conference on Distributed Computing Systems (ICDCS).

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

[6]  Saswati Sarkar,et al.  Pricing for profit in internet of things , 2015, 2015 IEEE International Symposium on Information Theory (ISIT).

[7]  Min Chen,et al.  NextMe: Localization Using Cellular Traces in Internet of Things , 2015, IEEE Transactions on Industrial Informatics.

[8]  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).

[9]  Shiwen Mao,et al.  DeepFi: Deep learning for indoor fingerprinting using channel state information , 2015, 2015 IEEE Wireless Communications and Networking Conference (WCNC).

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

[11]  Patrick Thiran,et al.  A General Framework for Sensor Placement in Source Localization , 2019, IEEE Transactions on Network Science and Engineering.

[12]  Geoffrey E. Hinton,et al.  ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.

[13]  Bo Yu,et al.  Convolutional Neural Networks for human activity recognition using mobile sensors , 2014, 6th International Conference on Mobile Computing, Applications and Services.

[14]  Shiwen Mao,et al.  DeepML: Deep LSTM for Indoor Localization with Smartphone Magnetic and Light Sensors , 2018, 2018 IEEE International Conference on Communications (ICC).

[15]  Paul Congdon,et al.  Avoiding multipath to revive inbuilding WiFi localization , 2013, MobiSys '13.

[16]  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).

[17]  Daniel Roggen,et al.  Deep Convolutional and LSTM Recurrent Neural Networks for Multimodal Wearable Activity Recognition , 2016, Sensors.

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

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

[20]  Shih-Hau Fang,et al.  Channel State Reconstruction Using Multilevel Discrete Wavelet Transform for Improved Fingerprinting-Based Indoor Localization , 2016, IEEE Sensors Journal.

[21]  Yunhao Liu,et al.  From RSSI to CSI , 2013, ACM Comput. Surv..

[22]  K. J. Ray Liu,et al.  Achieving Centimeter-Accuracy Indoor Localization on WiFi Platforms: A Frequency Hopping Approach , 2016, IEEE Internet of Things Journal.

[23]  Jiangchuan Liu,et al.  Robust Indoor Wireless Localization Using Sparse Recovery , 2017, 2017 IEEE 37th International Conference on Distributed Computing Systems (ICDCS).

[24]  Guigang Zhang,et al.  Deep Learning , 2016, Int. J. Semantic Comput..

[25]  Shiwen Mao,et al.  PhaseFi: Phase Fingerprinting for Indoor Localization with a Deep Learning Approach , 2014, 2015 IEEE Global Communications Conference (GLOBECOM).

[26]  Hong Li,et al.  Image and Attribute Based Convolutional Neural Network Inference Attacks in Social Networks , 2020, IEEE Transactions on Network Science and Engineering.

[27]  Matt W. Mutka,et al.  AirLoc: Mobile Robots Assisted Indoor Localization , 2015, 2015 IEEE 12th International Conference on Mobile Ad Hoc and Sensor Systems.

[28]  Jie Yang,et al.  E-eyes: device-free location-oriented activity identification using fine-grained WiFi signatures , 2014, MobiCom.

[29]  Chi Zhang,et al.  LiTell: robust indoor localization using unmodified light fixtures , 2016, MobiCom.

[30]  Yunhao Liu,et al.  PADS: Passive detection of moving targets with dynamic speed using PHY layer information , 2014, 2014 20th IEEE International Conference on Parallel and Distributed Systems (ICPADS).

[31]  Mingyan Liu,et al.  PhaseU: Real-time LOS identification with WiFi , 2015, 2015 IEEE Conference on Computer Communications (INFOCOM).

[32]  Yuanyuan Yang,et al.  A fine-grained indoor fingerprinting localization based on magnetic field strength and channel state information , 2017, Pervasive Mob. Comput..

[33]  Chao Yang,et al.  TensorBeat , 2017, ACM Trans. Intell. Syst. Technol..

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

[35]  Heinrich Meyr,et al.  Optimum receiver design for wireless broad-band systems using OFDM. I , 1999, IEEE Trans. Commun..

[36]  Sachin Katti,et al.  WiDeo: Fine-grained Device-free Motion Tracing using RF Backscatter , 2015, NSDI.

[37]  Romit Roy Choudhury,et al.  SurroundSense: mobile phone localization via ambience fingerprinting , 2009, MobiCom '09.

[38]  Shiwen Mao,et al.  CSI Phase Fingerprinting for Indoor Localization With a Deep Learning Approach , 2016, IEEE Internet of Things Journal.

[39]  Shiwen Mao,et al.  CSI-Based Fingerprinting for Indoor Localization: A Deep Learning Approach , 2016, IEEE Transactions on Vehicular Technology.

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

[41]  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)..

[42]  Dan Wu,et al.  Toward Centimeter-Scale Human Activity Sensing with Wi-Fi Signals , 2017, Computer.

[43]  Jie Xiong,et al.  ArrayTrack: A Fine-Grained Indoor Location System , 2011, NSDI.

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

[45]  Injong Rhee,et al.  FM-based indoor localization via automatic fingerprint DB construction and matching , 2013, MobiSys '13.