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)

Time-dependent KPI generation based on Copula

New generations of mobile networks are developed to serve the increasing user and devices connected to the networks. However, the management of these networks has a need of automation, due to the also growing complexity. Self-Organizing Network (SON) was conceived to fulfil the automation of network management, within which troubleshooting is located under Self-Healing (SH). The current tendency is the use of Artificial Intelligence (AI) algorithms that are trained using Machine Learning (ML). This training requires a considerable amount of data. Anyway, the reluctance of operators to sharing their data with the research community causes a scarcity of data representing degradations that can be used for the development and training of ML algorithms. In this paper a method to solve this data sample limitation is proposed. In the first place, the method divides the data into time categories to create models which preserve the time characteristics. Afterwards, it applies statistical copulas to adapt the models into new ones maintaining statistical relationships. Finally, the method returns synthetic data that can be an input for ML. As an example, the data from a real mobile network is processed.I Plan Propio de Investigación y Transferencia de la Universidad de Málag

Fusion of LTE and UWB ranges for trilateration

High precise indoor positioning is the spotlight for the new mobile generation 5G. Ultra-Wide Band (UWB) technology stands out as the creditable preference for locating the user in indoor scenarios. The principal limitation of this technology appears in the coverage area that reaches a few tens of meters. In our case of study, we have simulated a conceivable real environment with UWB and Long Term Evolution (LTE) base stations for positioning users. In this scenario, users have been tracked by an Extended Kalman Filter (EKF), a memory state filter to predict the movement of the user that improves the performance of the system. In regions that receivers only track isolated UWB stations we make use of this information in order to improve the location provided by mobile networks. Essentially, when performing trilateration using the data offered by LTE, we also include the data of UWB in case that this information do not serve to position by itself. In this manner, the coverage area by at least one UWB station augments and accuracy of the system improves in those regions where only LTE previously provided location.Financiado por la Unión Europea en el marco del acuerdo de subvención Horizonte 2020 (Grant 871249, LOCUS)

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

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

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

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

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

Variables psicológicas implicadas en la actitud e iniciativa emprendedora (II): personalidad, cognición y emoción

El proyecto titulado: Variables implicadas en la actitud e iniciativa emprendedora (II): personalidad, cognición y emoción, es la continuidad de otro presentado en la convocatoria anterior (2016-2017) cuyo objetivo era evaluar variables psicológicas en la actitud emprendedora de los estudiantes universitarios de la Universidad Complutense de Madrid (UCM). Este segundo proyecto ha tenido por objetivo principal ampliar la evaluación a otras facultades y áreas de conocimiento de nuestra universidad a fin de obtener el mapa y perfil de la iniciativa emprendedora del universitario UCM

5G for Construction: Use Cases and Solutions

The world is currently undergoing a new industrial revolution characterized by the digitization and automation of industry through the use of Information and Communication Technologies (ICTs). The construction sector is one of the largest sectors of the industry. Most of the tasks associated with this sector are carried out at worksites that are defined by their dynamism, decentralization, temporality, and the intervention of a large number of workers, subcontractors, machinery, equipment, and materials. These characteristics make this sector a great challenge for the implementation of ICTs. In this paper, the benefits of the use of the Fifth-Generation (5G) of mobile networks in the construction industry are presented. To that end, first, the digitization and automation needs of the sector are jointly analyzed, establishing different use cases and identifying the requirements of each one. Second, the main characteristics of 5G that address these use cases are identified. Third, a global framework for the application of 5G technology to the construction industry is proposed. Finally, an overview of some directions for future work are provided