9 documents found, page 1 of 1
Enabling dynamic indoor localization by employing intersection over union as a ...
Klus, Lucie; Klus, Roman; Torres-Sospedra, Joaquín; Lohan, Elena Simona; Silva, Ivo Miguel Menezes; Pendão, Cristiano Gonçalves; Valkama, MikkoIn modern wireless networks evolving towards 6th generation, localization, and sensing in indoor environments play an increasingly critical role in ensuring reliability, security, and control over network users, including vehicular assets. Despite recent advancements in deep learning, using k-Nearest Neighbors (k-NN) as a positioning algorithm in Received Signal Strength Indicator (RSSI) fingerprinting-based lo...
Autoencoder extreme learning machine for fingerprint-based positioning: A good ...
Gaibor, Darwin P. Quezada; Klus, Lucie; Klus, Roman; Lohan, Elena Simona; Nurmi, Jari; Valkama, Mikko; Huerta, Joaquín; Torres-Sospedra, JoaquínIndoor 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 o...
L/F-CIPS: Collaborative indoor positioning for smartphones with lateration and ...
Pascacio, Pavel; Torres-Sospedra, Joaquín; Casteleyn, Sven; Lohan, Elena Simona; Nurmi, JariThe demand for indoor location-based services and the wide availability of mobile devices have triggered research into new positioning systems able to provide accurate indoor positions using smartphones. However, accurate solutions require a complex implementation and long-term maintenance of their infrastructure. Collaborative systems may help to alleviate these drawbacks. In this paper, we propose a smartphon...
Scalable and efficient clustering for fingerprint-based positioning
Torres-Sospedra, Joaquín; Quezada Gaibor, Darwin P.; Nurmi, Jari; Koucheryavy, Yevgeni; Lohan, Elena Simona; Huerta, JoaquinIndoor positioning based on IEEE 802.11 wireless LAN (Wi-Fi) fingerprinting needs a reference data set, also known as a radio map, in order to match the incoming fingerprint in the operational phase with the most similar fingerprint in the data set and then estimate the device position indoors. Scalability problems may arise when the radio map is large, e.g., providing positioning in large geographical areas or...
A collaborative approach using neural networks for BLE-RSS lateration-based ind...
Pascacio, Pavel; Torres-Sospedra, Joaquín; Casteleyn, Sven; Lohan, Elena SimonaIn daily life, mobile and wearable devices with high computing power, together with anchors deployed in indoor en-vironments, form a common solution for the increasing demands for indoor location-based services. Within the technologies and methods currently in use for indoor localization, the approaches that rely on Bluetooth Low Energy (BLE) anchors, Received Signal Strength (RSS), and lateration are among the...
A comprehensive and reproducible comparison of clustering and optimization rule...
Torres-Sospedra, Joaquín; Richter, Philipp; Moreira, Adriano; Mendoza-Silva, Germán M.; Lohan, Elena Simona; Trilles, Sergio; Matey-Sanz, MiguelWi-Fi fingerprinting is a well-known technique used for indoor positioning. It relies on a pattern recognition method that compares the captured operational fingerprint with a set of previously collected reference samples (radio map) using a similarity function. The matching algorithms suffer from a scalability problem in large deployments with a huge density of fingerprints, where the number of reference sampl...
Applications and innovations on sensor-enabled wearable devices
Torres-Sospedra, Joaquín; Lohan, Elena Simona; Molinaro, Antonella; Moreira, Adriano; Rusu-Casandra, Alexandru; Smékal, ZdenekMultiple sensors are embedded in wearable devices [...].
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...
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 ...