Performance of Real-TimeWireless Communication for Railway Environments with IEEE 802.11p

IEEE 802.11p complements the widespread 802.11 standard for use in vehicular environments. Designed for communication between wireless devices in rapidly changing environments, it handles situations where connection and communication must be completed in very short periods of time. Even though this is supposed to be a substantial improvement and essential for real-time applications, latencies have been rarely investigated in existing studies. Based on practical experiments, we evaluate how beneficial 802.11p’s changes in comparison to regular 802.11n are and whether the usage of IEEE 802.11p is suitable within environments with real-time constraints. We compare latencies of networks in OCB mode to both networks in IBSS (ad-hoc) and BSS/AP (access point) mode by measuring the initial connection speed and the latency of ICMP packets’ round-trip times. Furthermore, the response of the latter to disturbances is measured. The results show OCB to be superior to both BSS/AP and IBSS modes in average latency, maximum latency, and standard deviation under all tested circumstances

Open Platforms for Connected Vehicles

L'abstract è presente nell'allegato / the abstract is in the attachmen

230501

Cooperative Vehicular Platooning (Co-VP) is a paradigmatic example of a Cooperative Cyber-Physical System (Co-CPS), which holds the potential to vastly improve road safety by partially removing humans from the driving task. However, the challenges are substantial, as the domain involves several topics, such as control theory, communications, vehicle dynamics, security, and traffic engineering, that must be coupled to describe, develop and validate these systems of systems accurately. This work presents a comprehensive survey of significant and recent advances in Co-VP relevant fields. We start by overviewing the work on control strategies and underlying communication infrastructures, focusing on their interplay. We also address a fundamental concern by presenting a cyber-security overview regarding these systems. Furthermore, we present and compare the primary initiatives to test and validate those systems, including simulation tools, hardware-in-the-loop setups, and vehicular testbeds. Finally, we highlight a few open challenges in the Co-VP domain. This work aims to provide a fundamental overview of highly relevant works on Co-VP topics, particularly by exposing their inter-dependencies, facilitating a guide that will support further developments in this challenging field.info:eu-repo/semantics/publishedVersio

Machine Learning-Powered Management Architectures for Edge Services in 5G Networks

L'abstract è presente nell'allegato / the abstract is in the attachmen

Sl-EDGE: Network Slicing at the Edge

Network slicing of multi-access edge computing (MEC) resources is expected to be a pivotal technology to the success of 5G networks and beyond. The key challenge that sets MEC slicing apart from traditional resource allocation problems is that edge nodes depend on tightly-intertwined and strictly-constrained networking, computation and storage resources. Therefore, instantiating MEC slices without incurring in resource over-provisioning is hardly addressable with existing slicing algorithms. The main innovation of this paper is Sl-EDGE, a unified MEC slicing framework that allows network operators to instantiate heterogeneous slice services (e.g., video streaming, caching, 5G network access) on edge devices. We first describe the architecture and operations of Sl-EDGE, and then show that the problem of optimally instantiating joint network-MEC slices is NP-hard. Thus, we propose near-optimal algorithms that leverage key similarities among edge nodes and resource virtualization to instantiate heterogeneous slices 7.5x faster and within 0.25 of the optimum. We first assess the performance of our algorithms through extensive numerical analysis, and show that Sl-EDGE instantiates slices 6x more efficiently then state-of-the-art MEC slicing algorithms. Furthermore, experimental results on a 24-radio testbed with 9 smartphones demonstrate that Sl-EDGE provides at once highly-efficient slicing of joint LTE connectivity, video streaming over WiFi, and ffmpeg video transcoding

Advanced Applications of Rapid Prototyping Technology in Modern Engineering

Rapid prototyping (RP) technology has been widely known and appreciated due to its flexible and customized manufacturing capabilities. The widely studied RP techniques include stereolithography apparatus (SLA), selective laser sintering (SLS), three-dimensional printing (3DP), fused deposition modeling (FDM), 3D plotting, solid ground curing (SGC), multiphase jet solidification (MJS), laminated object manufacturing (LOM). Different techniques are associated with different materials and/or processing principles and thus are devoted to specific applications. RP technology has no longer been only for prototype building rather has been extended for real industrial manufacturing solutions. Today, the RP technology has contributed to almost all engineering areas that include mechanical, materials, industrial, aerospace, electrical and most recently biomedical engineering. This book aims to present the advanced development of RP technologies in various engineering areas as the solutions to the real world engineering problems

Improvement on the Wi-Fi vehicle simulator ERA

In recent years, the boom generated by the IoT is allowing many technologies to move towards automation. One of the fields taking advantage of it is autonomous driving. What we can observe is that most vehicle manufacturers are using sensor redundancy to solve the problem of erroneous detection in order to achieve secure systems. However, it is inefficient as a solution in general. One approach that has been gaining momentum in recent years is collaborative perception ("Swarm Intelligence Technologies"), where the data that sensors capture between vehicles is transmitted, gaining a general overview of the situation. So, are "Swarm Intelligence Technologies" the future? Can we check this without having to invest millions of dollars in experimental vehicles, save ourselves from doing tests in cities and thus not having to endanger pedestrians? Is a simulator a good idea? How realistic is it? What are its limitations? These are all the different questions we asked ourselves when we started this project. In order to be able to demonstrate that these technologies are more optimal in V2X communications environments, it is necessary to experiment and contrast results. At the same time, being able to do tests and get results with different scenarios without increasing the cost of experimentation is important. In this project, you will notice that it starts with a tool called ERA that allows vehicular communication in a basic simulation environment called Gazebo. However, tool is quite simple and does not present any experiment that can be translated to a vehicle and that is why it was essential to move it to a more realistic environment. If we manage to adapt to an environment as realistic as possible, from there we can create many different experiments to check how the DSRC (Dedicated Short Range Communication) behaves and whether the solutions regarding vehicle perception are efficient and good. This will drastically shorten the time needed for us to live in a safer world with reliable autonomous vehicles. This project has started with the introduction to the tools needed for project development (ROS, C ++, Unreal Engine), experimenting with the current state of the ERA tool and understanding exactly what it does and how it achieves it. Afterwards, we created a base experiment in the new Carla environment with the code that binds the ERA tool to the simulator. Finally, we switched from V2V unicast to multicast communications (much more efficient). At last, an important comment is that the project included some concepts outside the simulation scope improvement to have more tools for future experimentation such as basic object detection and Operative Systems specific for UAVs (such as drones). These can be useful to have test cases on small vehicles on the future.En estos últimos años el boom que ha generado el IoT está permitiendo que muchas tecnologías avancen hacia la automatización. Uno de los campos donde más se ha visto beneficiado es el de la conducción autónoma. Lo que vemos es que la mayoría de fabricantes de vehículos utilizan redundancia en los sensores para solucionar el problema de detecciones erróneas y tener sistemas seguros, pero es poco eficiente como solución. Un planteamiento que está ganando fuerza los últimos años es el de la colaboración perceptiva entre vehículos ( "Swarm Intelligence Technologies"), donde se transmiten los datos que los vehiculos captan entre ellos, ganando una visión global de la situación. Entonces, son las "Swarm Intelligence Technologies" el futuro? Podemos comprobarlo sin tener que invertir million de dólares en vehículos experimentales, ahorrarnos hacer tests en ciudades y así no tener que poner en peligro a los peatones? Es un simulador una buena idea? Cuánto semejante es? ¿Cuáles son las limitaciones que presenta? Todas estas son las diferentes preguntas que nos hemos planteado al comenzar este proyecto. Para poder demostrar que las estas tecnologías son más óptimas en entornos de comunicaciones V2X hay que experimentar y contrastar resultados. Al mismo tiempo, poder hacer tests y obtener resultados con diferentes escenarios sin aumentar el coste de experimentación es importante. En este proyecto, observará que parto de una herramienta llamada ERA que permite la comunicación vehicular en un entorno de simulación básico como es Gazebo. Esta herramienta es bastante simple y no presenta ninguna situación que se pueda trasladar a un vehículo y es por eso que es esencial trasladarlo a algún entorno más realista. Si conseguimos adaptarnos a un entorno lo más objetivo posible, a partir de ahí se podrán crear numerosos y diferentes experimentos para comprobar como el DSRC (Dedicated Short Range Communication) se comporta y si las soluciones respecto a la percepción vehicular son eficientes y buenas. Esto permitirá acortar drásticamente el tiempo necesario para que podamos vivir en un mundo más seguro con vehículos autónomos fiables. Este proyecto ha empezado con la introducción a las herramientas necesarias para el desarrollo del proyecto (ROS, C ++, Unreal Engine), experimentando el estado actual de la herramienta ERA y entendiendo exactamente qué hace y cómo lo consigue. Después, se ha creado un experimento base al nuevo entorno Carla y el código que une la herramienta ERA al simulador. Finalmente, se ha pasado de comunicaciones unicast V2V comunicaciones V2V multicast (mucho más eficientes). Finalmente, un comentario importante es que el proyecto ha incluido un anexo fuera del proyecto para tener más herramientas para futuros experimentos como son la detección de objetos básicos y los sistemas operativos específicos para drones. Estos, pueden ser útiles para tener casos de prueba en vehículos en el futuro.En aquests darrers anys el boom que ha generat el IoT està permetent que moltes tecnologies avancin cap a la automatització. Un dels camps on més s'ha vist beneficiat és el de la conducció autònoma. El que veiem és que la majoria de fabricants de vehicles utilitzen redundància als sensors per a solucionar el problema de deteccions errònies i tenir sistemes segurs. Malgrat això, és poc eficient com a solució en general. Un plantejament que està guanyant força els últims anys és el de la col·laboració perceptiva entre vehicles ("Swarm Intelligence Technologies"), on es transmeten les dades que els vehicles capten entre ells, guanyant una visió global de la situació. Llavors, són les "Swarm Intelligence Technologies" el futur. Podem comprovar-ho sense haver d'invertir milions de dolars en vehicles experimentals, estalviar-nos fer tests en ciutats i així no haver de posar en perill als vianants? És un simulador una bona idea? Quant de semblant és? Quines són les limitacions que presenta. Totes aquestes són les diferents preguntes que ens hem plantejat al començar aquest projecte. Per a poder demostrar que les aquestes tecnologies són més optimes en entorns de comunicacions V2X cal experimentar i contrastar resultats. Al mateix temps, poder fer tests i obtenir resultats amb diferents escenaris sense augmentar el cost d'experimentació és important. En aquest projecte, observareu que es parteix del software ERA que permet la comunicació vehicular en un entorn de simulació bàsic com és Gazebo. Malgrat això, aquesta eina és força simple i no presenta cap situació que es pugui traslladar a un vehicle i és per això que és essencial traslladar-ho a algun entorn més realista. Si aconseguim adaptar-nos a un entorn el més objectiu possible, a partir d'aquí es podran crear nombrosos i diferents experiments per a comprovar com el DSRC (Dedicated Short Range Communication) es comporta i si les solucions respecte a la percepció vehicular són eficients i bones. Això permet escurçar dràsticament el temps necessari per a que puguem viure en un món més segur amb vehicles autonòms fiables. Aquest projecte ha començat amb la introducció a les eines necessàries per al desenvolupament del projecte (ROS, C ++, Unreal Engine), experimentant l'estat actual de l'eina ERA i entenent exactament què fa i com ho aconsegueix. Després, s'ha creat un experiment base al nou entorn Carla i el codi que uneix l'eina ERA al simulador. Finalment, s'ha passat de comunicacions unicast V2V a comunicacions V2V multicast (molt més eficients). Finalment, un comentari important és que el projecte ha inclòs un annex fora del projecte per a tenir més eines per a futurs experiments com ara la detecció d'objectes bàsics i els sistemes operatius específics per als drons. Aquests, poden ser útils per tenir casos de prova en vehicles en el futur

Waveform Design for 5G and beyond Systems

5G traffic has very diverse requirements with respect to data rate, delay, and reliability. The concept of using multiple OFDM numerologies adopted in the 5G NR standard will likely meet these multiple requirements to some extent. However, the traffic is radically accruing different characteristics and requirements when compared with the initial stage of 5G, which focused mainly on high-speed multimedia data applications. For instance, applications such as vehicular communications and robotics control require a highly reliable and ultra-low delay. In addition, various emerging M2M applications have sparse traffic with a small amount of data to be delivered. The state-of-the-art OFDM technique has some limitations when addressing the aforementioned requirements at the same time. Meanwhile, numerous waveform alternatives, such as FBMC, GFDM, and UFMC, have been explored. They also have their own pros and cons due to their intrinsic waveform properties. Hence, it is the opportune moment to come up with modification/variations/combinations to the aforementioned techniques or a new waveform design for 5G systems and beyond. The aim of this Special Issue is to provide the latest research and advances in the field of waveform design for 5G systems and beyond