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1
المؤلفون: Ramires, Moises, Torres-Sospedra, Joaquín, Moreira, Adriano
المساهمون: Universidade do Minho
مصطلحات موضوعية: Clustering, Scalability, Positioning, RSSI, Fingerprinting, Wi-Fi Fingerprinting, Engenharia e Tecnologia::Engenharia Eletrotécnica, Eletrónica e Informática, Science & Technology, Indústria, inovação e infraestruturas
الوصف: The core of fingerprinting is based on the uniqueness of the RF signature in a given location over time. In the offline phase, the fingerprints –the set of RSSI values from different anchors– are collected at given locations generating a radio map. In the online phase, a matching algorithm retrieves the most similar fingerprints from the radio map and computes the position estimate for every operational fingerprint. However, computing the similarities to all the samples in the radio map may be inefficient and not scale in those cases where the radio map is large. Previous attempts to alleviate the computational load rely on the segmentation of the radio map through smart clustering in the offline stage, and a two-step estimation process in the online stage. However, most of the clustering models applied are generic without any consideration about signal propagation and relevant fingerprints are often filtered, resulting in a higher positioning error. This paper introduces Strongest AP Set (SAS), a clustering model conceived for RSSI-based fingerprinting. The results show that SAS is not only able to reduce the computational cost, but also to provide better accuracy than the full model without clustering.
الوصف (مترجم): The authors gratefully acknowledge funding from FCT – Fundação para a Ciência e a Tecnologia within the R&D Units Project Scope: UIDB/00319/2020.
وصف الملف: application/pdf
العلاقة: Ramires, M., Torres-Sospedra, J., & Moreira, A. (2022, September). Accurate and Efficient Wi-Fi Fingerprinting-Based Indoor Positioning in Large Areas. In 2022 IEEE 96th Vehicular Technology Conference (VTC2022-Fall) (pp. 1-6). IEEE.; 978-1-6654-5469-8; 1090-3038; 2577-2465; 978-1-6654-5468-1; https://ieeexplore.ieee.org/abstract/document/10012985Test
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2مؤتمر
المؤلفون: Torres-Sospedra, Joaquín, Pendão, Cristiano, Silva, Ivo, Meneses, Filipe, Quezada-Gaibor, Darwin, Montoliu, Raúl, Crivello, Antonino, Barsocchi, Paolo, Pérez-Navarro, Antoni, Moreira, Adriano
مصطلحات موضوعية: Wi-Fi Fingerprinting, Received Signal Strength, k–Nearest Neighbor, Reproducibility, Replicability, Engenharia e Tecnologia::Engenharia Eletrotécnica, Eletrónica e Informática, Cidades e comunidades sustentáveis
الوصف: Microsoft proposed RADAR in 2000, the first indoor positioning system based on Wi-Fi fingerprinting. Since then, the indoor research community has worked not only to improve the base estimator but also on finding an optimal RSS data representation. The long-term objective is to find a positioning system that minimises the mean positioning error. Despite the relevant advances in the last 23 years, a disruptive solution has not been reached yet. The evaluation with non-open datasets and comparisons with non-optimized baselines make the analysis of the current status of fingerprinting for indoor positioning difficult. In addition, the lack of implementation details or data used for evaluation in several works make results reproducibility impossible. This paper focuses on providing a comprehensive analysis of fingerprinting with k-NN and settling the basement for replicability and reproducibility in further works, targeting to bring relevant information about k-NN when it is used as a baseline comparison of advanced fingerprint-based methods. ; The authors gratefully acknowledge funding from projects ORIENTATE H2020-MSCA-IF GA.101023072; FCT UIDB/00319/2020; CYTED Network “GeoLibero”; PID2021-122642OB-C42; and PID2021-122642OB-C44.
وصف الملف: application/pdf
العلاقة: info:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDB%2F00319%2F2020/PT; info:eu-repo/grantAgreement/EC/H2020/101023072/EU; https://ieeexplore.ieee.org/document/10332535Test; Torres-Sospedra, J., Pendão, C., Silva, I., Meneses, F., Quezada-Gaibor, D., Montoliu, R., … Moreira, A. (2023, September 25). Let’s Talk about k-NN for Indoor Positioning: Myths and Facts in RF-based Fingerprinting. 2023 13th International Conference on Indoor Positioning and Indoor Navigation (IPIN). IEEE. http://doi.org/10.1109/ipin57070.2023.10332535Test; https://hdl.handle.net/1822/90430Test; 979-8-3503-2011-4
الإتاحة: https://doi.org/10.1109/IPIN57070.2023.10332535Test
https://doi.org/10.1109/ipin57070.2023.10332535Test
https://hdl.handle.net/1822/90430Test -
3دورية أكاديمية
المؤلفون: Rajeev Kumar, Renu Popli, Vikas Khullar, Isha Kansal, Ashutosh Sharma
المصدر: Buildings, Vol 13, Iss 8, p 2048 (2023)
مصطلحات موضوعية: deep learning, federated learning, indoor localization, multi-labeled classification, multi-class classification, Wi-Fi fingerprinting, Building construction, TH1-9745
الوصف: For the establishment of future ubiquitous location-aware applications, a scalable indoor localization technique is essential technology. Numerous classification techniques for indoor localization exist, but none have proven to be as quick, secure, and dependable as what is now needed. This research proposes an effective and privacy-protective federated architecture-based framework for location classification via Wi-Fi fingerprinting. The federated indoor localization classification (f-ILC) system that was suggested had distributed client–server architecture with data privacy for any and all related edge devices or clients. To try and evaluate the proposed f-ILC framework, different data from different sources on the Internet were collected and given in a format that had already been processed. Experiments were conducted with standard learning, federated learning with a single client, and federated learning with several clients to make sure that federated deep learning models worked correctly. The success of the f-ILC framework was computed using a number of factors, such as validation of accuracy and loss. The results showed that the suggested f-ILC framework performed better than traditional distributed deep learning-based classifiers in terms of accuracy and loss while keeping data secure. Due to its innovative design and superior performance over existing classifier tools, edge devices’ data privacy makes this proposed architecture the ideal solution.
وصف الملف: electronic resource
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4
المؤلفون: Quezada-Gaibor, Darwin, Torres-Sospedra, Joaquín, Nurmi, Jari, Koucheryavy, Yevgeni, Huerta, Joaquín
المساهمون: Universidade do Minho
مصطلحات موضوعية: generative networks, indoor positioning, machine learning, Wi-Fi fingerprinting, Ciências Naturais::Ciências da Computação e da Informação, Science & Technology
الوصف: Indoor Positioning based on Machine Learning has drawn increasing attention both in the academy and the industry as meaningful information from the reference data can be extracted. Many researchers are using supervised, semi-supervised, and unsupervised Machine Learning models to reduce the positioning error and offer reliable solutions to the end-users. In this article, we propose a new architecture by combining Convolutional Neural Network (CNN), Long short-term memory (LSTM) and Generative Adversarial Network (GAN) in order to increase the training data and thus improve the position accuracy. The proposed combination of supervised and unsupervised models was tested in 17 public datasets, providing an extensive analysis of its performance. As a result, the positioning error has been reduced in more than 70% of them.
الوصف (مترجم): The authors gratefully acknowledge funding from European Union’s Hori zon 2020 Research and Innovation programme under the Marie Skłodowska Curie grant agreements No. 813278 (A-WEAR: A network for dynamic wearable applications with privacy constraints, http://www.a-wear.euTest/) and No. 101023072 (ORIENTATE: Low-cost Reliable Indoor Positioning in Smart Factories, http://orientate.dsi.uminho.ptTest).
وصف الملف: application/pdf
العلاقة: D. Quezada-Gaibor, J. Torres-Sospedra, J. Nurmi, Y. Koucheryavy and J. Huerta, "SURIMI: Supervised Radio Map Augmentation with Deep Learning and a Generative Adversarial Network for Fingerprint-based Indoor Positioning," 2022 IEEE 12th International Conference on Indoor Positioning and Indoor Navigation (IPIN), Beijing, China, 2022, pp. 1-8, doi: 10.1109/IPIN54987.2022.9918146.; 978-1-7281-6219-5; 2162-7347; 2471-917X; 978-1-7281-6218-8; https://ieeexplore.ieee.org/document/9918146Test
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5
المؤلفون: Quezada-Gaibor, Darwin, Torres-Sospedra, Joaquín, Nurmi, Jari, Koucheryavy, Yevgeni, Huerta, Joaquin
المساهمون: Universidade do Minho
مصطلحات موضوعية: Indoor Localisation, Wi-Fi fingerprinting, deep learning, extreme learning machine, Ciências Naturais::Ciências da Computação e da Informação, Science & Technology
الوصف: Machine learning models have become an essential tool in current indoor positioning solutions, given their high capabilities to extract meaningful information from the environment. Convolutional neural networks (CNNs) are one of the most used neural networks (NNs) due to that they are capable of learning complex patterns from the input data. Another model used in indoor positioning solutions is the Extreme Learning Machine (ELM), which provides an acceptable generalization performance as well as a fast speed of learning. In this paper, we offer a lightweight combination of CNN and ELM, which provides a quick and accurate classification of building and floor, suitable for power and resource-constrained devices. As a result, the proposed model is 58% faster than the benchmark, with a slight improvement in the classification accuracy (by less than 1%).
الوصف (مترجم): The authors gratefully acknowledge funding from European Union's Horizon 2020 Research and Innovation programme under the Marie Sklodowska Curie grant agreements No. 813278 (A-WEAR: A network for dynamic wearable applications with privacy constraints, http://www.a-wear.euTest/) and No. 101023072 (ORIENTATE: Low-cost Reliable Indoor Positioning in Smart Factories, http://orientate.dsi.uminho.ptTest).
وصف الملف: application/pdf
العلاقة: Quezada-Gaibor, D., Torres-Sospedra, J., Nurmi, J., Koucheryavy, Y., & Huerta, J. (2022, June 7). Lightweight Hybrid CNN-ELM Model for Multi-building and Multi-floor Classification. 2022 International Conference on Localization and GNSS (ICL-GNSS). IEEE. http://doi.org/10.1109/icl-gnss54081.2022.9797021Test; 9781665405751; 2325-0747; https://ieeexplore.ieee.org/document/9797021Test
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6
المؤلفون: Quezada-Gaibor, Darwin, Klus, Lucie, Torres-Sospedra, Joaquín, Simona Lohan, Elena, Nurmi, Jari, Granell, Carlos, Huerta, Joaquin
المساهمون: Universidade do Minho
مصطلحات موضوعية: Data cleansing, Data pre-processing, Indoor positioning, Localisation, Wi-Fi Fingerprinting, Science & Technology
الوصف: Wearable and IoT devices requiring positioning and localisation services grow in number exponentially every year. This rapid growth also produces millions of data entries that need to be pre-processed prior to being used in any indoor positioning system to ensure the data quality and provide a high Quality of Service (QoS) to the end-user. In this paper, we offer a novel and straightforward data cleansing algorithm for WLAN fingerprinting radio maps. This algorithm is based on the correlation among fingerprints using the Received Signal Strength (RSS) values and the Access Points (APs)'s identifier. We use those to compute the correlation among all samples in the dataset and remove fingerprints with low level of correlation from the dataset. We evaluated the proposed method on 14 independent publicly-available datasets. As a result, an average of 14% of fingerprints were removed from the datasets. The 2D positioning error was reduced by 2.7% and 3D positioning error by 5.3% with a slight increase in the floor hit rate by 1.2% on average. Consequently, the average speed of position prediction was also increased by 14%.
الوصف (مترجم): The authors gratefully acknowledge funding from European Union's Horizon 2020 Research and Innovation programme under the Marie Sklodowska Curie grant agreements No. 813278 (A-WEAR: A network for dynamic wearable applications with privacy constraints, http://www.a-wear.euTest/) and No. 101023072 (ORIENTATE: Low-cost Reliable Indoor Positioning in Smart Factories, http://orientate.dsi.uminho.ptTest).
وصف الملف: application/pdf
العلاقة: D. Quezada-Gaibor et al., "Data Cleansing for Indoor Positioning Wi-Fi Fingerprinting Datasets," 2022 23rd IEEE International Conference on Mobile Data Management (MDM), Paphos, Cyprus, 2022, pp. 349-354, doi: 10.1109/MDM55031.2022.00079; 9781665451765; 1551-6245; https://ieeexplore.ieee.org/document/9861169Test
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7دورية أكاديمية
المؤلفون: Gaibor, Darwin P. Quezada, Klus, Lucie, Klus, Roman, Lohan, Elena Simona, Nurmi, Jari, Valkama, Mikko, Huerta, Joaquín, Torres-Sospedra, Joaquín
مصطلحات موضوعية: Autoencoder, Extreme learning machine, Indoor positioning, Singular value decomposition, Weight initialization, Wi-Fi fingerprinting, Engenharia e Tecnologia::Engenharia Eletrotécnica, Eletrónica e Informática
الوصف: Indoor positioning based on machine-learning (ML) models has attracted widespread interest in the last few years, given its high performance and usability. Supervised, semisupervised, and unsupervised models have thus been widely used in this field, not only to estimate the user position, but also to compress, clean, and denoise fingerprinting datasets. Some scholars have focused on developing, improving, and optimizing ML models to provide accurate solutions to the end user. This article introduces a novel method to initialize the input weights in autoencoder extreme learning machine (AE-ELM), namely factorized input data (FID), which is based on the normalized form of the orthogonal component of the input data. AE-ELM with FID weight initialization is used to efficiently reduce the radio map. Once the dimensionality of the dataset is reduced, we use k -nearest neighbors to perform the position estimation. This research work includes a comparative analysis with several traditional ways to initialize the input weights in AE-ELM, showing that FID provide a significantly better reconstruction error. Finally, we perform an assessment with 13 indoor positioning datasets collected from different buildings and in different countries. We show that the dimensionality of the datasets can be reduced more than 11 times on average, while the positioning error suffers only a small increment of 15% (on average) in comparison to the baseline.
وصف الملف: application/pdf
العلاقة: info:eu-repo/grantAgreement/EC/H2020/101023072/EU; https://ieeexplore.ieee.org/document/10195972Test; D. P. Q. Gaibor et al., "Autoencoder extreme learning machine for fingerprint-based positioning: A good weight initialization is decisive," in IEEE Journal of Indoor and Seamless Positioning and Navigation, vol. 1, pp. 53-68, 2023, doi:10.1109/JISPIN.2023.3299433.; https://hdl.handle.net/1822/87189Test
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8دورية أكاديمية
المؤلفون: Zhengyan Zhang, Yue Yu, Liang Chen, Ruizhi Chen
المصدر: Remote Sensing; Volume 15; Issue 14; Pages: 3520
مصطلحات موضوعية: indoor positioning system, acoustic ranging, Wi-Fi fingerprinting, data and model dual-driven, adaptive unscented Kalman filter
جغرافية الموضوع: agris
الوصف: An accurate indoor positioning system (IPS) for the public has become an essential function with the fast development of smart city-related applications. The performance of the current IPS is limited by the complex indoor environments, the poor performance of smartphone built-in sensors, and time-varying measurement errors of different location sources. This paper introduces a hybrid indoor positioning system (H-IPS) that combines acoustic ranging, Wi-Fi fingerprinting, and low-cost sensors. This system is designed specifically for large-scale indoor environments with non-line-of-sight (NLOS) conditions. To improve the accuracy in estimating pedestrian motion trajectory, a data and model dual-driven (DMDD) model is proposed to integrate the inertial navigation system (INS) mechanization and the deep learning-based speed estimator. Additionally, a double-weighted K-nearest neighbor matching algorithm enhanced the accuracy of Wi-Fi fingerprinting and scene recognition. The detected scene results were then utilized for NLOS detection and estimation of acoustic ranging results. Finally, an adaptive unscented Kalman filter (AUKF) was developed to provide universal positioning performance, which further improved by the Wi-Fi accuracy indicator and acoustic drift estimator. The experimental results demonstrate that the presented H-IPS achieves precise positioning under NLOS scenes, with meter-level accuracy attainable within the coverage range of acoustic signals.
وصف الملف: application/pdf
العلاقة: AI Remote Sensing; https://dx.doi.org/10.3390/rs15143520Test
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9مؤتمر
المؤلفون: Torres-Sospedra, Joaquín, Pendão, Cristiano, Silva, Ivo, Meneses, Filipe, Quezada Gaibor, Darwin, Montoliu Colás, Raul, Crivello, Antonino, Barsocchi, Paolo, Pérez-Navarro, Antoni, Moreira, Adriano
مصطلحات موضوعية: Wi-Fi Fingerprinting, Received Signal Strength, k-Nearest Neighbor, Reproducibility, Replicability, Indoor navigation, Radar, Fingerprint recognition, Reproducibility of results, Wireless fidelity, Indoor positioning systems
الوصف: Microsoft proposed RADAR in 2000, the first indoor positioning system based on Wi-Fi fingerprinting. Since then, the indoor research community has worked not only to improve the base estimator but also on finding an optimal RSS data representation. The long-term objective is to find a positioning system that minimises the mean positioning error. Despite the relevant advances in the last 23 years, a disruptive solution has not been reached yet. The evaluation with non-open datasets and comparisons with non-optimized baselines make the analysis of the current status of fingerprinting for indoor positioning difficult. In addition, the lack of implementation details or data used for evaluation in several works make results reproducibility impossible. This paper focuses on providing a comprehensive analysis of fingerprinting with k-NN and settling the basement for replicability and reproducibility in further works, targeting to bring relevant information about k-NN when it is used as a baseline comparison of advanced fingerprint-based methods.
وصف الملف: application/pdf
العلاقة: 13th International Conference on Indoor Positioning and Indoor Navigation (IPIN), Nuremberg, Germany, 2023; https://ieeexplore.ieee.org/abstract/document/10332535Test; J. Torres-Sospedra et al., "Let’s Talk about k-NN for Indoor Positioning: Myths and Facts in RF-based Fingerprinting," 2023 13th International Conference on Indoor Positioning and Indoor Navigation (IPIN), Nuremberg, Germany, 2023, pp. 1-6, doi:10.1109/IPIN57070.2023.10332535.; http://hdl.handle.net/10234/207240Test
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10مؤتمر
المؤلفون: Quezada-Gaibor, Darwin, Torres-Sospedra, Joaquín, Nurmi, Jari, Koucheryavy, Yevgeni, Huerta, Joaquín
مصطلحات موضوعية: Indoor Positioning, Wi-Fi fingerprinting, Clustering, Computing Efficiency
الوصف: A preprint version of the paper entitled “Lightweight Wi-Fi Fingerprinting with a Novel RSS Clustering Algorithm”, presented in the 2021 International Conference on Indoor Positioning and Indoor Navigation (IPIN). Nowadays, several indoor positioning solutions support Wi-Fi and use this technology to estimate the user position. It is characterized by its low cost, availability in indoor and outdoor environments, and a wide variety of devices support Wi-Fi technology. However, this technique suffers from scalability problems when the radio map has a large number of reference fingerprints because this might increase the time response in the operational phase. In order to minimize the time response, many solutions have been proposed along the time. The most common solution is to divide the data set into clusters. Thus, the incoming fingerprint will be compared with a specific number of samples grouped by, for instance, similarity (clusters). Many of the current studies have proposed a variety of solutions based on the modification of traditional clustering algorithms in order to provide a better distribution of samples and reduce the computational load. This work proposes a new clustering method based on the maximum Received Signal Strength (RSS) values to join similar fingerprints. As a result, the proposed fingerprinting clustering method outperforms three of the most well-known clustering algorithms in terms of processing time at the operational phase of fingerprinting. ; The authors gratefully acknowledge funding from European Union's Horizon 2020 Research and Innovation programme under the Marie Sklodowska Curie grant agreement No. 813278 (A-WEAR: A network for dynamic wearable applications with privacy constraints, http://www.a-wear.euTest/).; J. Torres-Sospedra gratefully acknowledge funding from Ministerio de Ciencia, Innovación y Universidades (INSIGNIA, PTQ2018-009981)
العلاقة: info:eu-repo/grantAgreement/EC/H2020/813278/; https://zenodo.org/communities/a_wearTest; https://zenodo.org/communities/tau-tltposTest; https://zenodo.org/record/6767482Test; https://doi.org/10.1109/IPIN51156.2021.9662612Test; oai:zenodo.org:6767482
النص الكامل