SCALABLE AND EFFICIENT VERTICAL HANDOVER DECISION ALGORITHMS IN VEHICULAR NETWORK CONTEXTS

A finales de los años noventa, y al comienzo del nuevo milenio, las redes inalámbricas han evolucionado bastante, pasando de ser sólo una tecnología prometedora para convertirse en un requisito para las actividades cotidianas en las sociedades desarrolladas. La infraestructura de transporte también ha evolucionado, ofreciendo comunicación a bordo para mejorar la seguridad vial y el acceso a contenidos de información y entretenimiento. Los requisitos de los usuarios finales se han hecho dependientes de la tecnología, lo que significa que sus necesidades de conectividad han aumentado debido a los diversos requisitos de las aplicaciones que se ejecutan en sus dispositivos móviles, tales como tabletas, teléfonos inteligentes, ordenadores portátiles o incluso ordenadores de abordo (On-Board Units (OBUs)) dentro de los vehículos. Para cumplir con dichos requisitos de conectividad, y teniendo en cuenta las diferentes redes inalámbricas disponibles, es necesario adoptar técnicas de Vertical Handover (VHO) para cambiar de red de forma transparente y sin necesidad de intervención del usuario. El objetivo de esta tesis es desarrollar algoritmos de decisión (Vertical Handover Decision Algorithms (VHDAs)) eficientes y escalables, optimizados para el contexto de las redes vehiculares. En ese sentido se ha propuesto, desarrollado y probado diferentes algoritmos de decisión basados en la infraestructura disponible en las actuales, y probablemente en las futuras, redes inalámbricas y redes vehiculares. Para ello se han combinado diferentes técnicas, métodos computacionales y modelos matemáticos, con el fin de garantizar una conectividad apropiada, y realizando el handover hacia las redes más adecuadas de manera a cumplir tanto con los requisitos de los usuarios como los requisitos de las aplicaciones. Con el fin de evaluar el contexto, se han utilizado diferentes herramientas para obtener información variada, como la disponibilidad de la red, el estado de la red, la geolocalizaciónMárquez Barja, JM. (2012). SCALABLE AND EFFICIENT VERTICAL HANDOVER DECISION ALGORITHMS IN VEHICULAR NETWORK CONTEXTS [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/17869Palanci

Accurate Landing of Unmanned Aerial Vehicles Using Ground Pattern Recognition

[EN] Over the last few years, several researchers have been developing protocols and applications in order to autonomously land unmanned aerial vehicles (UAVs). However, most of the proposed protocols rely on expensive equipment or do not satisfy the high precision needs of some UAV applications such as package retrieval and delivery or the compact landing of UAV swarms. Therefore, in this work, a solution for high precision landing based on the use of ArUco markers is presented. In the proposed solution, a UAV equipped with a low-cost camera is able to detect ArUco markers sized 56×56 cm from an altitude of up to 30 m. Once the marker is detected, the UAV changes its flight behavior in order to land on the exact position where the marker is located. The proposal was evaluated and validated using both the ArduSim simulation platform and real UAV flights. The results show an average offset of only 11 cm from the target position, which vastly improves the landing accuracy compared to the traditional GPS-based landing, which typically deviates from the intended target by 1 to 3 m.This work was funded by the Ministerio de Ciencia, Innovación y Universidades, Programa Estatal de Investigación, Desarrollo e Innovación Orientada a los Retos de la Sociedad, Proyectos I+D+I 2018 , Spain, under Grant RTI2018-096384-B-I00.Wubben, J.; Fabra Collado, FJ.; Tavares De Araujo Cesariny Calafate, CM.; Krzeszowski, T.; Márquez Barja, JM.; Cano, J.; Manzoni, P. (2019). Accurate Landing of Unmanned Aerial Vehicles Using Ground Pattern Recognition. Electronics. 8(12):1-16. https://doi.org/10.3390/electronics8121532S116812Pan, X., Ma, D., Jin, L., & Jiang, Z. (2008). Vision-Based Approach Angle and Height Estimation for UAV Landing. 2008 Congress on Image and Signal Processing. doi:10.1109/cisp.2008.78Tang, D., Li, F., Shen, N., & Guo, S. (2011). UAV attitude and position estimation for vision-based landing. Proceedings of 2011 International Conference on Electronic & Mechanical Engineering and Information Technology. doi:10.1109/emeit.2011.6023131Gautam, A., Sujit, P. B., & Saripalli, S. (2014). A survey of autonomous landing techniques for UAVs. 2014 International Conference on Unmanned Aircraft Systems (ICUAS). doi:10.1109/icuas.2014.6842377Holybro Pixhawk 4 · PX4 v1.9.0 User Guidehttps://docs.px4.io/v1.9.0/en/flight_controller/pixhawk4.htmlGarrido-Jurado, S., Muñoz-Salinas, R., Madrid-Cuevas, F. J., & Medina-Carnicer, R. (2016). Generation of fiducial marker dictionaries using Mixed Integer Linear Programming. Pattern Recognition, 51, 481-491. doi:10.1016/j.patcog.2015.09.023Romero-Ramirez, F. J., Muñoz-Salinas, R., & Medina-Carnicer, R. (2018). Speeded up detection of squared fiducial markers. Image and Vision Computing, 76, 38-47. doi:10.1016/j.imavis.2018.05.004ArUco: Augmented reality library based on OpenCVhttps://sourceforge.net/projects/aruco/Jin, S., Zhang, J., Shen, L., & Li, T. (2016). On-board vision autonomous landing techniques for quadrotor: A survey. 2016 35th Chinese Control Conference (CCC). doi:10.1109/chicc.2016.7554984Chen, X., Phang, S. K., Shan, M., & Chen, B. M. (2016). System integration of a vision-guided UAV for autonomous landing on moving platform. 2016 12th IEEE International Conference on Control and Automation (ICCA). doi:10.1109/icca.2016.7505370Nowak, E., Gupta, K., & Najjaran, H. (2017). Development of a Plug-and-Play Infrared Landing System for Multirotor Unmanned Aerial Vehicles. 2017 14th Conference on Computer and Robot Vision (CRV). doi:10.1109/crv.2017.23Shaker, M., Smith, M. N. R., Yue, S., & Duckett, T. (2010). Vision-Based Landing of a Simulated Unmanned Aerial Vehicle with Fast Reinforcement Learning. 2010 International Conference on Emerging Security Technologies. doi:10.1109/est.2010.14Araar, O., Aouf, N., & Vitanov, I. (2016). Vision Based Autonomous Landing of Multirotor UAV on Moving Platform. Journal of Intelligent & Robotic Systems, 85(2), 369-384. doi:10.1007/s10846-016-0399-zPatruno, C., Nitti, M., Petitti, A., Stella, E., & D’Orazio, T. (2018). A Vision-Based Approach for Unmanned Aerial Vehicle Landing. Journal of Intelligent & Robotic Systems, 95(2), 645-664. doi:10.1007/s10846-018-0933-2Baca, T., Stepan, P., Spurny, V., Hert, D., Penicka, R., Saska, M., … Kumar, V. (2019). Autonomous landing on a moving vehicle with an unmanned aerial vehicle. Journal of Field Robotics, 36(5), 874-891. doi:10.1002/rob.21858De Souza, J. P. C., Marcato, A. L. M., de Aguiar, E. P., Jucá, M. A., & Teixeira, A. M. (2019). Autonomous Landing of UAV Based on Artificial Neural Network Supervised by Fuzzy Logic. Journal of Control, Automation and Electrical Systems, 30(4), 522-531. doi:10.1007/s40313-019-00465-ySITL Simulator (Software in the Loop)http://ardupilot.org/dev/docs/sitl-simulator-software-in-the-loop.htmlFabra, F., Calafate, C. T., Cano, J.-C., & Manzoni, P. (2017). On the impact of inter-UAV communications interference in the 2.4 GHz band. 2017 13th International Wireless Communications and Mobile Computing Conference (IWCMC). doi:10.1109/iwcmc.2017.7986413MAVLink Micro Air Vehicle Communication Protocolhttp://qgroundcontrol.org/mavlink/startFabra, F., Calafate, C. T., Cano, J. C., & Manzoni, P. (2018). ArduSim: Accurate and real-time multicopter simulation. Simulation Modelling Practice and Theory, 87, 170-190. doi:10.1016/j.simpat.2018.06.009Careem, M. A. A., Gomez, J., Saha, D., & Dutta, A. (2019). HiPER-V: A High Precision Radio Frequency Vehicle for Aerial Measurements. 2019 16th Annual IEEE International Conference on Sensing, Communication, and Networking (SECON). doi:10.1109/sahcn.2019.882490

FLEXNET: Flexible Networks for IoT based services

Internet of Things is becoming one of the main triggers in designing and deploying new services aiming at fulfilling the wide demand imposed by end-users. Usually, concrete solutions addressing the optimization of the wireless segment are found in the literature. However, it is much less frequent to find end-to-end solutions to be easily adopted by the corresponding stakeholders. It is in this context that FLEXNET brings an integrated solution, relying on cutting-edge technologies, dealing with a wide set of technical requirements imposed by the different applications and services.This work was supported by FLEXNET Project: "Flexible IoT Networks for Value Creators" (Celtic 2016/3), in the Eureka Celtic-Next Cluster

An Overview of Vertical Handover Techniques: Algorithms, Protocols and Tools

[EN] Wireless technologies, under the "Anywhere, Anytime" paradigm, offer users the promise of being always attached to the network. Mobile devices enabled with multiple wireless technologies make possible to maintain seamless connectivity in highly dynamic scenarios such as vehicular networks (VNs), switching from one wireless network to another by using vertical handover techniques (VHO). In this paper we present an overview of VHO techniques, along with the main algorithms, protocols and tools proposed in the literature. In addition we suggest the most appropriate VHO techniques to efficiently communicate in VN environments considering the particular characteristics of this type of networks. (C) 2010 Elsevier B.V. All rights reserved.This work was partially supported by the Ministerio de Educación y Ciencia, Spain, under Grant TIN2008-06441-C02-01, and by the Ayudas complementarias para proyectos de I+D para grupos de calidad de la Generalitat Valenciana (ACOMP/2010/005).Márquez Barja, JM.; Tavares De Araujo Cesariny Calafate, CM.; Cano Escribá, JC.; Manzoni, P. (2011). An Overview of Vertical Handover Techniques: Algorithms, Protocols and Tools. COMPUTER COMMUNICATIONS. 34(8):985-997. doi:10.1016/j.comcom.2010.11.010S98599734