Using Synthetic Data to Enhance the Accuracy of Fingerprint-Based Localization: A Deep Learning Approach

Article


Nabati, M., Navidan, H., Shahbazian, R., Ghorashi, S. A. and Windridge, D. 2020. Using Synthetic Data to Enhance the Accuracy of Fingerprint-Based Localization: A Deep Learning Approach. IEEE Sensors Letters. 4 (Art. 6000204). https://doi.org/10.1109/LSENS.2020.2971555
AuthorsNabati, M., Navidan, H., Shahbazian, R., Ghorashi, S. A. and Windridge, D.
Abstract

Human-centered data collection is typically costly and implicates issues of privacy. Various solutions have been proposed in the literature to reduce this cost, such as crowd-sourced data collection, or the use of semisupervised algorithms. However, semisupervised algorithms require a source of unlabeled data, and crowd-sourcing methods require numbers of active participants. An alternative passive data collection modality is fingerprint-based localization. Such methods use received signal strength or channel state information in wireless sensor networks to localize users in indoor/outdoor environments. In this letter, we introduce a novel approach to reduce training data collection costs in fingerprint-based localization by using synthetic data. Generative adversarial networks (GANs) are used to learn the distribution of a limited sample of collected data and, following this, to produce synthetic data that can be used to augment the real collected data in order to increase overall positioning accuracy. Experimental results on a benchmark dataset show that by applying the proposed method and using a combination of 10% collected data and 90% synthetic data, we can obtain essentially similar positioning accuracy to that which would be obtained by using the full set of collected data. This means that by employing GAN-generated synthetic data, we can use 90% less real data, thereby reducing data-collection costs while achieving acceptable accuracy.

KeywordsSensor applications; deep learning; fingerprint localization; generative adversarial networks (GANs); synthetic data; wireless sensor networks
JournalIEEE Sensors Letters
Journal citation4 (Art. 6000204)
ISSN2475-1472
Year2020
PublisherIEEE
Accepted author manuscript
License
File Access Level
Anyone
Digital Object Identifier (DOI)https://doi.org/10.1109/LSENS.2020.2971555
Web address (URL)https://doi.org/10.1109/LSENS.2020.2971555
Publication dates
Print04 Feb 2020
Publication process dates
Accepted20 Jan 2020
Deposited01 Jun 2020
Copyright holder© 2020 IEEE
Copyright informationPersonal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
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