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Let’s talk about k-NN for indoor positioning: Myths and facts in RF-based finge...
Torres-Sospedra, Joaquín; Pendão, Cristiano; Silva, Ivo; Meneses, Filipe; Quezada-Gaibor, Darwin; Montoliu, Raúl; Crivello, Antonino; Barsocchi, PaoloMicrosoft 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 s...
Cloud platforms for context-adaptive positioning and localisation in GNSS-denie...
Quezada-Gaibor, Darwin; Torres-Sospedra, Joaquín; Nurmi, Jari; Koucheryavy, Yevgeni; Huerta, JoaquinCloud Computing and Cloud Platforms have become an essential resource for businesses, due to their advanced capabilities, performance, and functionalities. Data redundancy, scalability, and security, are among the key features offered by cloud platforms. Location-Based Services (LBS) often exploit cloud platforms to host positioning and localisation systems. This paper introduces a systematic review of current ...
SURIMI: supervised radio map augmentation with deep learning and a generative a...
Quezada-Gaibor, Darwin; Torres-Sospedra, Joaquín; Nurmi, Jari; Koucheryavy, Yevgeni; Huerta, JoaquínIndoor 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 combini...
Off-line evaluation of indoor positioning systems in different scenarios: the e...
Potorti, Francesco; Torres-Sospedra, Joaquín; Quezada-Gaibor, Darwin; Jimenez, Antonio Ramon; Seco, Fernando; Perez-Navarro, Antoni; Ortiz, MiguelEvery year, for ten years now, the IPIN competition has aimed at evaluating real-world indoor localisation systems by testing them in a realistic environment, with realistic movement, using the EvAAL framework. The competition provided a unique overview of the state-of-the-art of systems, technologies, and methods for indoor positioning and navigation purposes. Through fair comparison of the performance achieve...
Data cleansing for indoor positioning Wi-Fi fingerprinting datasets
Quezada-Gaibor, Darwin; Klus, Lucie; Torres-Sospedra, Joaquín; Simona Lohan, Elena; Nurmi, Jari; Granell, Carlos; Huerta, JoaquinWearable 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 algor...
Towards accelerated localization performance across indoor positioning datasets
Klus, Lucie; Quezada-Gaibor, Darwin; Torres-Sospedra, Joaquín; Lohan, Elena Simona; Granell, Carlos; Nurmi, JariThe localization speed and accuracy in the indoor scenario can greatly impact the Quality of Experience of the user. While many individual machine learning models can achieve comparable positioning performance, their prediction mechanisms offer different complexity to the system. In this work, we propose a fingerprinting positioning method for multi-building and multi-floor deployments, composed of a cascade of...
Lightweight hybrid CNN-ELM model for multi-building and multi-floor classification
Quezada-Gaibor, Darwin; Torres-Sospedra, Joaquín; Nurmi, Jari; Koucheryavy, Yevgeni; Huerta, JoaquinMachine 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 Lear...
Towards ubiquitous indoor positioning: comparing systems across heterogeneous d...
Torres-Sospedra, Joaquín; Silva, Ivo Miguel Menezes; Klus, Lucie; Quezada-Gaibor, Darwin; Crivello, Antonino; Barsocchi, Paolo; Pendão, CristianoThe evaluation of Indoor Positioning Systems (IPSs) mostly relies on local deployments in the researchers' or partners' facilities. The complexity of preparing comprehensive experiments, collecting data, and considering multiple scenarios usually limits the evaluation area and, therefore, the assessment of the proposed systems. The requirements and features of controlled experiments cannot be generalized since ...
Ensembling multiple radio maps with dynamic noise in fingerprint-based indoor p...
Torres-Sospedra, Joaquín; Aranda, Fernando J.; Alvarez, Fernando J.; Quezada-Gaibor, Darwin; Silva, Ivo Miguel Menezes; Pendão, Cristiano GonçalvesFingerprint-based indoor positioning is widely used in many contexts, including pedestrian and autonomous vehicles navigation. Many approaches have used traditional Machine Learning models to deal with fingerprinting, being k-NN the most common used one. However, the reference data (or radio map) is generally limited, as data collection is a very demanding task, which degrades overall accuracy. In this work, we...