Victim Detection and Localization in Emergencies

Detecting and locating victims in emergency scenarios comprise one of the most powerful tools to save lives. Fast actions are crucial for victims because time is running against them. Radio devices are currently omnipresent within the physical proximity of most people and allow locating buried victims in catastrophic scenarios. In this work, we present the benefits of using WiFi Fine Time Measurement (FTM), Ultra-Wide Band (UWB), and fusion technologies to locate victims under rubble. Integrating WiFi FTM and UWB in a drone may cover vast areas in a short time. Moreover, the detection capacity of WiFi and UWB for finding individuals is also compared. These findings are then used to propose a method for detecting and locating victims in disaster scenarios.This work was performed in the framework of the Horizon 2020 project LOCUS (Grant Agreement Number 871249), receiving funds from the European Union. This work was also partially funded by Junta de Andalucia (Project PY18-4647:PENTA)

Evaluación de la latencia de NB-IoT con medidas reales

In the 3GPP LTE Release 13, NB-IoT was standardized to provide wide-area connectivity for IoT. To optimize network signaling and power consumption, control plane (CP) optimization was introduced. In Release 15, to support infrequent small data transmissions, Early Data Transmission (EDT) was also included, in which the data are sent during the random access procedure. Thus, this paper analyses the latency performance of the different NB-IoT optimizations for the CP. The study, carried out in a real equipment, has been performed for different packet sizes and coverage levels. Evaluation results show lower latencies for EDT, particularly with small packets, where a reduced transport block is used, being more efficient from a network point of view. Additionally, we verify that EDT, unlike Release 13 optimization, fulfills 3GPP latency requirement for extreme coverage.Este trabajo ha sido parcialmente financiado por el Ministerio de Asuntos Económicos y Transformación Digital y la Unión Europea – NextGenerationEU, en el marco del Plan de Recuperación, Transformación y Resiliencia y el Mecanismo de Recuperación y Resiliencia bajo el proyecto MAORI y, por la Junta de Andalucía mediante el proyecto EDEL4.0 (UMA-18-FEDERJA-172). Se agradece también la financiación parcial de la Universidad de Málaga con el II Plan Propio de Investigación y Transferencia. Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tec

Evaluación de los modos de conexión para NB-IoT

In the 3GPP LTE Release 13, NB-IoT was standardized to provide wide-area connectivity for IoT. To optimize network signalling and power consumption, control plane (CP) and user plane (UP) optimizations were introduced. Also, to support infrequent small data transmissions, in Release 15 Early Data Transmissions (EDT) was introduced, where the data is sent during the random access procedure. Therefore, this paper analyses the latency performance of the different NB-IoT optimizations. The study, which has been carried out in NS-3, has been performed for different packet sizes. Evaluation results show that with low packet size, EDT with CP provides lower latency. However, with higher packet sizes, user plane solutions provide better latency.Este trabajo ha sido parcialmente financiado por la Junta de Andalucía mediante los proyectos AECMA-5G (UMA-CEIATECH-14) y EDEL4.0 (UMA-18-FEDERJA-172). Se agradece también la financiación parcial de la Universidad de Málaga con el Plan Propio de Investigación y Transferencia. Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

UWB and WiFi characterization for localization in construction sites

High-precision location is becoming a necessity in the future Industry 4.0 applications that will come up in the near future. However, the construction sector remains particularly obsolete in the adoption of Industry 4.0 applications. In this work we study the accuracy and penetration capacity of two technologies that are expected to deal with future high-precision location services such as Ultra Wide Band (UWB) and WiFi Fine Time Measurement (FTM).For this, a measurement campaign has been done in a construction environment, where UWB and WiFi-FTM setups have been deployed. The performance of UWB and WiFi-FTM have been compared with a prior set of indoors measurements. Moreover, the impact of fusion of location technologies has been assesed to measure the potential improvements in the construction scenario.This work has been carried out through the I plan Propio de Investigación y Transferencia y Divulgación Científica by University of Malaga and the Junta de Andalucía under the UMA-CEIATECH-12 TEDES-5G grant agreement. Moreover, this work has been performed in the framework of the Horizon 2020 project LOCUS (grant agreement number 871249), receiving funds from the European Comission. In addition, we would like to thank the company ACR for providing us access to a real construction environment. Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

Aplicación móvil para localización de interior mediante fusión de tecnologías

High-precision indoor location has become a necessity for the new location-based services that are emerging around 5G. The European LOCUS project is a research Project that seeks to achieve high-precision location based on the 5G network. This work reflects one of the proof-of-concepts of the LOCUS project in which we propose the opportunistic fusion of different technologies, such as UltraWide Band (UWB) and WiFi Fine Time Measurement (FTM), to improve location accuracy. For this purpose, an experimental setup has been settle to validate the presented system, using both technologies due to their incorporation in the latest smartphones on the market. In this way, the use of fusion in trilateration is validated as an algorithm that significantly improves the positioning error by overdetermining the localization problem and also improves the coverage area.Este trabajo se ha realizado en el marco del proyecto Horizonte 2020 LOCUS (acuerdo de subvención n. 871249), recibiendo fondos de la Unión Europea y ha sido parcialmente apoyado por la Universidad de Málaga a través del I plan Propio de Investigación y Transferencia y a través de la Junta de Andalucía bajo el acuerdo de subvención UMACEIATECH-12 TEDES-5G. Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

Detección de degradaciones en redes móviles basado en máquina de estados y umbrales de Otsu

Troubleshooting degradations in a mobile network is mainly a manual activity. Self-Healing (SH) appeared to automate such functions, using techniques such as Data Analytics with network performance indicators. Along this line, this work proposes an algorithm to detect degradations, analysing network metrics. The algorithm is based on a state machine that follows the status of the network over time series of indicators. This algorithm only requieres the specification of few parameters; some of them calculated using Otsu’s thresholding. Finally, the performance of the algorithm has been checked using data obtained from a LTE microcell.Este trabajo ha sido financiado parcialmente por la Universidad de Málaga, Campus de Excelencia Internacional Andalucía Tech, a través del II Plan Propio de Investigación y Transferencia. Además, también está parcialmente financiado por la Junta de Andalucía a través del proyecto PY18-4647:PENTA

Posicionamiento 5G con mapas radio incompletos.

Precise positioning will play a key role in future 5G/6G services. The upcoming location-based services drive the necessity of high-precision positioning to indoors. In fingerprinting, which is the most commonly used indoor location algorithm, comprehensive radio maps are essential for a precise localization service and highly influence on the result of the final position of the user. A robust algorithm that supports missing information from the map may improve the robustness and reliability of the localization service. In this work, we compare the performance of fingerprinting and different decision tree (DTR) and Adaboost (DTA and LTA) based regressors in a real 5G scenario with missing information. Additionally, we demonstrate the robustness of the LTA method, which had the highest performance among the tested approaches.Este trabajo se ha realizado en el marco del proyecto Maori (acuerdo de subvención número TSI-063000-2021-53) financiado por la Unión Europea- NextGenerationEU. Además, también ha sido parcialmente financiado por la Universidad de Málaga, Campus de Excelencia Internacional Andalucia Tech

Herramienta de diagnosis para redes móviles basada en puntos sigmas de correlaciones

Mobile network users are demanding with regard to the quality of the services, forcing the operators to solve the network degradations in the shortest time possible. For this purpose, a method for root cause diagnosis of degradations is proposed. It is based on different correlations (among CMs and a KPI) and the weighted average of sigma points to provide a ranking of possible causal candidates. Finally, this method is tested on metrics obtained from a commercial equipment.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech