Listar por autoría "a8fa3a83-7b77-488f-8d6b-16c06e76e31a"

Mostrando ítems 1-17 de 17

    • openAccess   Autoencoder Extreme Learning Machine for Fingerprint-Based Positioning: A Good Weight Initialization is Decisive 

      Quezada Gaibor, Darwin; Klus, Lucie; Klus, Roman; Lohan, Elena Simona; Nurmi, Jari; Valkama, Mikko; Huerta, Joaquin IEEE (2023-07-27)
      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 ...

    • openAccess   Cloud Platforms for Context-Adaptive Positioning and Localisation in GNSS-Denied Scenarios—A Systematic Review 

      Quezada Gaibor, Darwin; Torres-Sospedra, Joaquín; Nurmi, Jari; Koucheryavy, Yevgeni; Huerta, Joaquin MDPI (2021-12-24)
      Cloud 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 ...

    • openAccess   Collaborative Indoor Positioning Systems: A Systematic Review 

      Pascacio, Pavel; Casteleyn, Sven; Torres-Sospedra, Joaquín; Lohan, Elena Simona; Nurmi, Jari MDPI (2021)
      Research and development in Collaborative Indoor Positioning Systems (CIPSs) is growing steadily due to their potential to improve on the performance of their non-collaborative counterparts. In contrast to the outdoors ...

    • closedAccess   Data Cleansing for Indoor Positioning Wi-Fi Fingerprinting Datasets 

      Quezada Gaibor, Darwin; Klus, Lucie; Torres-Sospedra, Joaquín; Lohan, Elena Simona; Nurmi, Jari; Granell, Carlos; Huerta, Joaquin IEEE (2022)
      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 ...

    • openAccess   Direct lightweight temporal compression for wearable sensor data 

      Klus, Lucie; Klus, Roman; Lohan, Elena Simona; Granell, Carlos; Talvitie, Jukka; Valkama, Mikko; Nurmi, Jari IEEE (2021-02-01)
      Emerging technologies enable massive deployment of wireless sensor networks across many industries. Internet of Things (IoT) devices are often deployed in critical infrastructure or health monitoring and require fast ...

    • openAccess   EWOk: Towards Efficient Multidimensional Compression of Indoor Positioning Datasets (Epub ahead of print) 

      Klus, Lucie; Klus, Roman; Torres-Sospedra, Joaquín; Lohan, Elena Simona; Granell, Carlos; Nurmi, Jari Institute of Electrical and Electronics Engineers Inc. (2023-05-17)
      Indoor positioning performed directly at the end-user device ensures reliability in case the network connection fails but is limited by the size of the RSS radio map necessary to match the measured array to the device’s ...

    • openAccess   Improving DBSCAN for Indoor Positioning Using Wi-Fi Radio Maps in Wearable and IoT Devices 

      Quezada Gaibor, Darwin; Klus, Lucie; Torres-Sospedra, Joaquín; Lohan, Elena Simona; Nurmi, Jari; Huerta, Joaquin IEEE (2020-10-05)
      IoT devices and wearables may rely on Wi-Fi finger-printing to estimate the position indoors. The limited resources of these devices make it necessary to provide adequate methods to reduce the operational computational ...

    • openAccess   L/F-CIPS: collaborative indoor positioning for smartphones with lateration and fingerprinting 

      Pascacio, Pavel; Torres-Sospedra, Joaquín; Casteleyn, Sven; Lohan, Elena Simona; Nurmi, Jari (2023-08-29)
      The demand for indoor location-based services (LBS) and the wide availability of mobile devices have triggered research into new positioning systems able to provide accurate indoor positions using smartphones. However, ...

    • closedAccess   Lightweight Hybrid CNN-ELM Model for Multi-building and Multi-floor Classification 

      Quezada Gaibor, Darwin; Torres-Sospedra, Joaquín; Nurmi, Jari; Koucheryavy, Yevgeni; Huerta, Joaquin IEEE (2022)
      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) ...

    • openAccess   Lightweight Wi-Fi Fingerprinting with a Novel RSS Clustering Algorithm 

      Quezada Gaibor, Darwin; Torres-Sospedra, Joaquín; Nurmi, Jari; Koucheryavy, Yevgeni; Huerta, Joaquin IEEE (2021-11-29)
      Nowadays, several indoor positioning solutions sup-port 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 ...

    • openAccess   Lossy Compression Methods for Performance-Restricted Wearable Devices 

      Klus, Lucie; Lohan, Elena Simona; Granell, Carlos; Nurmi, Jari CEUR Workshop Proceedings (2020)
      With the increasing popularity, diversity, and utilization of wearable devices, the data transfer-andstorage efficiency becomes increasingly important. This paper evaluates a set of compression techniques regarding their ...

    • openAccess   New Cluster Selection and Fine-grained Search for k-Means Clustering and Wi-Fi Fingerprinting 

      Torres-Sospedra, Joaquín; Quezada Gaibor, Darwin; Mendoza-Silva, Germán Martín; Nurmi, Jari; Koucheryavy, Yevgeni; Huerta, Joaquin IEEE (2020-06-02)
      Wi-Fi fingerprinting is a popular technique for Indoor Positioning Systems (IPSs) thanks to its low complexity and the ubiquity of WLAN infrastructures. However, this technique may present scalability issues when the ...

    • openAccess   RSS Fingerprinting Dataset Size Reduction Using Feature-Wise Adaptive k-Means Clustering 

      Klus, Lucie; Quezada Gaibor, Darwin; Torres-Sospedra, Joaquín; Lohan, Elena Simona; Granell, Carlos; Nurmi, Jari IEEE (2020-10)
      Modern IoT devices, that include smartphones and wearables, usually have limited resources. They require efficient methods to optimize the use of internal storage, provide computational efficiency, and reduce energy ...

    • openAccess   Scalable and Efficient Clustering for Fingerprint-Based Positioning 

      Torres-Sospedra, Joaquín; Quezada Gaibor, Darwin; Nurmi, Jari; Koucheryavy, Yevgeni; Lohan, Elena Simona; Huerta, Joaquin IEEE (2023)
      Abstract: Indoor 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 ...

    • closedAccess   Towards Accelerated Localization Performance Across Indoor Positioning Datasets 

      Klus, Lucie; Quezada Gaibor, Darwin; Torres-Sospedra, Joaquín; Lohan, Elena Simona; Granell, Carlos; Nurmi, Jari IEEE (2022)
      The 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 ...

    • openAccess   Towards Ubiquitous Indoor Positioning: Comparing Systems across Heterogeneous Datasets 

      Torres-Sospedra, Joaquín; Silva, Ivo; Klus, Lucie; Quezada Gaibor, Darwin; Crivello, Antonino; Barsocchi, Paolo; Pendão, Cristiano; Lohan, Elena Simona; Nurmi, Jari; Moreira, Adriano IEEE (2021-11-29)
      The 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 ...

    • openAccess   TUJI1 Dataset: Multi-device dataset for indoor localization with high measurement density 

      Klus, Lucie; Klus, Roman; Lohan, Elena Simona; Nurmi, Jari; Granell, Carlos; Valkama, Mikko; Talvitie, Jukka; Casteleyn, Sven; Torres-Sospedra, Joaquín Elsevier (2024-03-22)
      Positioning in indoor scenarios using signals of opportunity is an effective solution enabling accurate and reliable performance in Global Navigation Satellite System (GNSS)-obscured scenarios. Despite the availability of ...