Indoor Localization Using Data Augmentation via Selective Generative Adversarial Networks

Njima, Wafa; Chafii, Marwa; Chorti, Arsenia; Shubair, Raed M.; Poor, H. Vincent

doi:10.1109/access.2021.3095546

Cited by 60 publications

(53 citation statements)

References 34 publications

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“…Instead, our approach generates RSSI measurements and its corresponding new positions to cover new areas. Furthermore, we do not use GANs to generate fake RSSI vectors to be pseudo-labeled later as conducted in our previous work [36]. Such labeling process increases the computational complexity of the whole system, and the error on pseudo-labels prediction can lead to localization accuracy loss.…”

Section: B Contributionsmentioning

confidence: 99%

“…Proposed system System proposed in [36] FIGURE 1: The proposed weighted GAN based localization method vs. the system proposed in [36].…”

Section: Online Posi�on Es�ma�onmentioning

confidence: 99%

“…To address this issue, data augmentation based on GANs is proposed as a second approach in this work, in order to generate fake data which compliment real labeled collected data. In our previous work [36], a system combining selective GANs and semi-supervised learning is proposed to perform location prediction based on real collected data and fake selected-generated pseudo-labeled data. This system generates RSSI vectors to be pseudo-labeled from which we select the most realistic-fake pseudo-labeled positions.…”

Section: Online Posi�on Es�ma�onmentioning

confidence: 99%

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DNN-Based Indoor Localization Under Limited Dataset Using GANs and Semi-Supervised Learning

2022

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Indoor localization techniques based on supervised learning deliver great performance accuracy while maintaining low online complexity. However, such systems require massive amounts of data for offline training, which necessitates costly measurements. The essence of this paper is twofold with the purpose of providing solutions to missing data of different nature: available unlabeled data and missing unlabeled data. In both cases, we rely on a few labeled available data, which is costly yet insufficient to achieve a high localization accuracy. To address the problem of available unlabeled data, a weighted semisupervised DNN-based indoor localization approach leveraging pseudo-labeling methods in combination with real labeled samples and inexpensive pseudo-labeled samples is proposed in order to boost localization accuracy, while overcoming the high cost of collecting additional labeled data. As for the extreme case of unavailable unlabeled data, we propose an alternative localization system generating fake fingerprints based on generative adversarial networks (GANs) named 'Weighted GAN based indoor localization'. Furthermore, a deep neural network is trained on a mixed dataset containing both real collected and fake produced data samples using a similar weighting technique in order to improve location prediction performance and avoids overfitting. In terms of localization accuracy, our proposed localization approaches outperform conventional supervised localization schemes utilizing the same collection of real labeled samples. For experimental evaluations, we have tested our proposed methods on both simulated data and experimental data from the publicly available UJIndoorLoc database, which is built to test indoor positioning systems relying on Wi-Fi fingerprints. Results based on experimental data provide the localization accuracy increase compared to the classical supervised learning method using the same set of labeled collected data when using the weighted semi-supervised and the weighted-GAN approaches by 10.11 % and 8.53 %, respectively.

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Section: B Contributionsmentioning

confidence: 99%

“…Proposed system System proposed in [36] FIGURE 1: The proposed weighted GAN based localization method vs. the system proposed in [36].…”

Section: Online Posi�on Es�ma�onmentioning

confidence: 99%

Section: Online Posi�on Es�ma�onmentioning

confidence: 99%

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DNN-Based Indoor Localization Under Limited Dataset Using GANs and Semi-Supervised Learning

2022

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“…In [37], Njima et al proposed generative adversarial networks for the RSSI data augmentation to generate fake RSSI data based on a small set of real collected labeled data. The developed model utilizes the semi-supervised learning in order to predict the pseudo-labels of the generated RSSI.…”

Section: Related Workmentioning

confidence: 99%

A Proposal of the Fingerprint Optimization Method for the Fingerprint-Based Indoor Localization System with IEEE 802.15.4 Devices

et al. 2022

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Nowadays, human indoor localization services inside buildings or on underground streets are in strong demand for various location-based services. Since conventional GPS cannot be used, indoor localization systems using wireless technologies have been extensively studied. Previously, we studied a fingerprint-based indoor localization system using IEEE802.15.4 devices, called FILS15.4, to allow use of inexpensive, tiny, and long-life transmitters. However, due to the narrow channel band and the low transmission power, the link quality indicator (LQI) used for fingerprints easily fluctuates by human movements and other uncontrollable factors. To improve the localization accuracy, FILS15.4 restricts the detection granularity to one room in the field, and adopts multiple fingerprints for one room, considering fluctuated signals, where their values must be properly adjusted. In this paper, we present a fingerprint optimization method for finding the proper fingerprint parameters in FILS15.4 by extending the existing one. As the training phase using the measurement LQI, it iteratively changes fingerprint values to maximize the newly defined score function for the room detecting accuracy. Moreover, it automatically increases the number of fingerprints for a room if the accuracy is not sufficient. For evaluations, we applied the proposed method to the measured LQI data using the FILS15.4 testbed system in the no. 2 Engineering Building at Okayama University. The validation results show that it improves the average detection accuracy (at higher than 97%) by automatically increasing the number of fingerprints and optimizing the values.

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“…In this work, similar to work in [53], we use the log-distance path loss model formulated as follows:…”

Section: Performance Evaluationmentioning

confidence: 99%

Federated Distillation based Indoor Localization for IoT Networks

Etiabi¹,

Chafii²,

Amhoud³

2022

Preprint

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Federated distillation (FD) paradigm has been recently proposed as a promising alternative to federated learning (FL) especially in wireless sensor networks with limited communication resources. However, all state-of-the art FD algorithms are designed for only classification tasks and less attention has been given to regression tasks. In this work, we propose an FD framework that properly operates on regression learning problems. Afterwards, we present a use-case implementation by proposing an indoor localization system that shows a good trade-off communication load vs. accuracy compared to federated learning (FL) based indoor localization. With our proposed framework, we reduce the number of transmitted bits by up to 98%. Moreover, we show that the proposed framework is much more scalable than FL, thus more likely to cope with the expansion of wireless networks.

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Indoor Localization Using Data Augmentation via Selective Generative Adversarial Networks

Cited by 60 publications

References 34 publications

DNN-Based Indoor Localization Under Limited Dataset Using GANs and Semi-Supervised Learning

DNN-Based Indoor Localization Under Limited Dataset Using GANs and Semi-Supervised Learning

A Proposal of the Fingerprint Optimization Method for the Fingerprint-Based Indoor Localization System with IEEE 802.15.4 Devices

Federated Distillation based Indoor Localization for IoT Networks

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