Persona:
Castillo Cara, José Manuel

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ORCID
0000-0002-2990-7090
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Castillo Cara
Nombre de pila
José Manuel
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2016 - 20194
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Autor: García Varea, Ismael × search.filters.applied.f.itemDepartamento: Inteligencia Artificial ×

Resultados de la búsqueda

Mostrando 1 - 4 de 4
  • Miniatura
    Publicación
    Spatial statistical analysis for the design of indoor particle-filter-based localization mechanisms
    (SAGE, 2016-08-24) Martínez Gómez, Jesús; Martínez del Horno, Miguel; Brea Luján, Victor Manuel; Orozco Barbosa, Luis; García Varea, Ismael; Castillo Cara, José Manuel
    The accurate localization of end-users and resources is seen as one of the main pillars toward the successful implementation of context-based applications. While current outdoor localization mechanisms fulfill most application requirements, the design of accurate indoor localization mechanisms is still an open issue. Most research efforts are focusing on the design of mechanisms making use of the receiver signal strength indications generated by WLAN (wireless local area network) devices. However, the accuracy and robustness of such mechanisms can be severely compromised due to the random and unpredictable nature of radio channels. In this article, we develop a methodology incorporating various algorithms capable of coping with the unpredictable nature of radio channels. Following a holistic approach, we start by identifying the wireless equipment parameter setting, better meeting the implementation requirements of a robust indoor localization mechanism. We then make use of RANdom SAmple Consensus paradigm: a robust model-fitting mechanism capable of smoothing the data captured during the space survey. Using an experimental setup, we evaluate the benefits of integrating the floor plan and an ordinary Kriging interpolation algorithm in the estimation process. Our main findings show that our proposal can greatly improve the quality of the information to be used in the development of particle-filter-based indoor localization mechanisms.
  • Miniatura
    Publicación
    An Analysis of Multiple Criteria and Setups for Bluetooth Smartphone-Based Indoor Localization Mechanism
    (Hindawi, 2017-10-23) Lovón Melgarejo, Jesús; Bravo Rocca, Gusseppe; Orozco Barbosa, Luis; García Varea, Ismael; Castillo Cara, José Manuel
    Bluetooth Low Energy (BLE) 4.0 beacons will play a major role in the deployment of energy-efficient indoor localization mechanisms. Since BLE4.0 is highly sensitive to fast fading impairments, numerous ongoing studies are currently exploring the use of supervised learning algorithm as an alternative approach to exploit the information provided by the indoor radio maps. Despite the large number of results reported in the literature, there are still many open issues on the performance evaluation of such approach. In this paper, we start by identifying, in a simple setup, the main system parameters to be taken into account on the design of BLE4.0 beacons-based indoor localization mechanisms. In order to shed some light on the evaluation process using supervised learning algorithm, we carry out an in-depth experimental evaluation in terms of the mean localization error, local prediction accuracy, and global prediction accuracy. Based on our results, we argue that, in order to fully assess the capabilities of supervised learning algorithms, it is necessary to include all the three metrics.
  • Miniatura
    Publicación
    Comparative Study of Supervised Learning and Metaheuristic Algorithms for the Development of Bluetooth-Based Indoor Localization Mechanisms
    (Institute of Electrical and Electronics Engineers, 2019-02-15) Lovón Melgarejo, Jesús; Huarcaya Canal, Oscar; Orozco Barbosa, Luis; García Varea, Ismael; Castillo Cara, José Manuel
    The development of the Internet of Things (IoT) benefits from 1) the connections between devices equipped with multiple sensors; 2) wireless networks and; 3) processing and analysis of the gathered data. The growing interest in the use of IoT technologies has led to the development of numerous diverse applications, many of which are based on the knowledge of the end user's location and profile. This paper investigates the characterization of Bluetooth signals behavior using 12 different supervised learning algorithms as a first step toward the development of fingerprint-based localization mechanisms. We then explore the use of metaheuristics to determine the best radio power transmission setting evaluated in terms of accuracy and mean error of the localization mechanism. We further tune-up the supervised algorithm hyperparameters. A comparative evaluation of the 12 supervised learning and two metaheuristics algorithms under two different system parameter settings provide valuable insights into the use and capabilities of the various algorithms on the development of indoor localization mechanisms.
  • Miniatura
    Publicación
    An Empirical Study of the Transmission Power Setting for Bluetooth-Based Indoor Localization Mechanisms
    (MDPI, 2017-06-07) Lovón Melgarejo, Jesús; Bravo Rocca, Gusseppe; Orozco Barbosa, Luis; García Varea, Ismael; Castillo Cara, José Manuel
    Nowadays, there is a great interest in developing accurate wireless indoor localization mechanisms enabling the implementation of many consumer-oriented services. Among the many proposals, wireless indoor localization mechanisms based on the Received Signal Strength Indication (RSSI) are being widely explored. Most studies have focused on the evaluation of the capabilities of different mobile device brands and wireless network technologies. Furthermore, different parameters and algorithms have been proposed as a means of improving the accuracy of wireless-based localization mechanisms. In this paper, we focus on the tuning of the RSSI fingerprint to be used in the implementation of a Bluetooth Low Energy 4.0 (BLE4.0) Bluetooth localization mechanism. Following a holistic approach, we start by assessing the capabilities of two Bluetooth sensor/receiver devices. We then evaluate the relevance of the RSSI fingerprint reported by each BLE4.0 beacon operating at various transmission power levels using feature selection techniques. Based on our findings, we use two classification algorithms in order to improve the setting of the transmission power levels of each of the BLE4.0 beacons. Our main findings show that our proposal can greatly improve the localization accuracy by setting a custom transmission power level for each BLE4.0 beacon.