Pseudo Label-Driven Federated Learning-Based Decentralized Indoor Localization via Mobile Crowdsourcing

Received signal strength (RSS) fingerprint-based indoor localization has received increasing popularity over the past decades. However, it suffers from the high calibration effort for fingerprint collection. In this paper, a Centralized indooR localizatioN method using Pseudo-label (CRNP) is proposed, which employs a small set of labeled data (RSS fingerprint) along with large volumes of unlabeled data (RSS values without coordinates) to reduce the workload of labeled data collection and improve the indoor localization performance. However, the rich location data is large in quantity and privacy sensitive, which may lead to high network cost (i.e., data transmission cost, data storage cost) and potential privacy leakage for data transmission to the central server. Therefore, a decentralized indoor localization method incorporating CRNP and federated learning is devised, which keeps the location data on local users’ devices and improves the shared CRNP model by aggregating users’ updates of the model. The experiment results demonstrate that (i) the proposed CRNP enables to improve the indoor localization accuracy by using unlabeled crowdsourced data; (ii) the designed decentralized scheme is robust to different data distribution and is capable to reduce the network cost and prevent users’ privacy leakage.

[1]  Tin Kam Ho,et al.  Probabilistic radio-frequency fingerprinting and localization on the run , 2014, Bell Labs Technical Journal.

[2]  Gi-Wan Yoon,et al.  Building a Practical Wi-Fi-Based Indoor Navigation System , 2014, IEEE Pervasive Computing.

[3]  Mu Zhou,et al.  Robust Neighborhood Graphing for Semi-Supervised Indoor Localization With Light-Loaded Location Fingerprinting , 2018, IEEE Internet of Things Journal.

[4]  Geoffrey E. Hinton,et al.  Reducing the Dimensionality of Data with Neural Networks , 2006, Science.

[5]  Dan Wang,et al.  TDFI: Two-stage Deep Learning Framework for Friendship Inference via Multi-source Information , 2019, IEEE INFOCOM 2019 - IEEE Conference on Computer Communications.

[6]  Shueng-Han Gary Chan,et al.  Tilejunction: Mitigating Signal Noise for Fingerprint-Based Indoor Localization , 2016, IEEE Transactions on Mobile Computing.

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

[8]  M. Shamim Hossain,et al.  Proactive Cache-Based Location Privacy Preserving for Vehicle Networks , 2018, IEEE Wireless Communications.

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

[10]  Blaise Agüera y Arcas,et al.  Communication-Efficient Learning of Deep Networks from Decentralized Data , 2016, AISTATS.

[11]  Yiqiang Chen,et al.  Semi-supervised deep extreme learning machine for Wi-Fi based localization , 2015, Neurocomputing.

[12]  Sebastian Ruder,et al.  An overview of gradient descent optimization algorithms , 2016, Vestnik komp'iuternykh i informatsionnykh tekhnologii.

[13]  Yuan Shao,et al.  A semi-supervised deep learning approach towards localization of crowdsourced data , 2019, ACM TUR-C.

[14]  Salil S. Kanhere,et al.  Participatory Sensing: Crowdsourcing Data from Mobile Smartphones in Urban Spaces , 2011, 2011 IEEE 12th International Conference on Mobile Data Management.

[15]  Kin K. Leung,et al.  A Survey of Indoor Localization Systems and Technologies , 2017, IEEE Communications Surveys & Tutorials.

[16]  Adolfo Martínez Usó,et al.  UJIIndoorLoc: A new multi-building and multi-floor database for WLAN fingerprint-based indoor localization problems , 2014, 2014 International Conference on Indoor Positioning and Indoor Navigation (IPIN).

[17]  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.

[18]  Michal R. Nowicki,et al.  Low-effort place recognition with WiFi fingerprints using deep learning , 2016, AUTOMATION.

[19]  Hsin-Piao Lin,et al.  Applying Deep Neural Network (DNN) for Robust Indoor Localization in Multi-Building Environment , 2018, Applied Sciences.

[20]  Solmaz Niknam,et al.  Federated Learning for Wireless Communications: Motivation, Opportunities, and Challenges , 2019, IEEE Communications Magazine.

[21]  Yi Liu,et al.  Indoor Fingerprint Positioning Based on Wi-Fi: An Overview , 2017, ISPRS Int. J. Geo Inf..

[22]  Mung Chiang,et al.  Indoor Location Estimation with Reduced Calibration Exploiting Unlabeled Data via Hybrid Generative/Discriminative Learning , 2012, IEEE Transactions on Mobile Computing.

[23]  Dong-Hyun Lee,et al.  Pseudo-Label : The Simple and Efficient Semi-Supervised Learning Method for Deep Neural Networks , 2013 .

[24]  Geoffrey E. Hinton,et al.  Rectified Linear Units Improve Restricted Boltzmann Machines , 2010, ICML.

[25]  Shueng-Han Gary Chan,et al.  Sectjunction: Wi-Fi indoor localization based on junction of signal sectors , 2014, 2014 IEEE International Conference on Communications (ICC).

[26]  Hamed Haddadi,et al.  Deep Learning in Mobile and Wireless Networking: A Survey , 2018, IEEE Communications Surveys & Tutorials.

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

[28]  José Manuel Páez-Borrallo,et al.  A New Location Estimation System for Wireless Networks Based on Linear Discriminant Functions and Hidden Markov Models , 2006, EURASIP J. Adv. Signal Process..

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

[30]  Laurence T. Yang,et al.  Indoor smartphone localization via fingerprint crowdsourcing: challenges and approaches , 2016, IEEE Wireless Communications.