V2I Applications in Highways: How RSU Dimensioning Can Improve Service Delivery

This paper investigates the performance of Vehicle-to-Infrastructure (V2I) services over Vehicular Networks (VANETs) that are assisted by Road Side Units (RSU). More specifically, an analytical study of RSU dimensioning and a respective module is designed and developed in a simulated VANET environment. Two V2I application scenarios (e.g. car crash, spot weather) are considered in order to evaluate the impact of RSUs, vehicles’ size and speed and car crash start time and duration on applications’ performance. It is shown that the VANET network metrics (Packet Loss and Packet Delivery Ratio) are affected by the available MAC Bit rates and application scenarios. Mobility model metrics (Total Busy Time and Total CO2 Emissions) are also affected by the different application scenarios, number and type of vehicles

Neural Network Based Vehicular Location Prediction Model for Cooperative Active Safety Systems

Safety systems detect unsafe conditions and provide warnings for travellers to take action and avoid crashes. Estimation of the geographical location of a moving vehicle as to where it will be positioned next with high precision and short computation time is crucial for identifying dangers. To this end, navigational and dynamic data of a vehicle are processed in connection with the data received from neighbouring vehicles and infrastructure in the same vicinity. In this study, a vehicular location prediction model was developed using an artificial neural network for cooperative active safety systems. The model is intended to have a constant, shorter computation time as well as higher accuracy features. The performance of the proposed model was measured with a real-time testbed developed in this study. The results are compared with the performance of similar studies and the proposed model is shown to deliver a better performance than other models.</p

Spectral Efficiency and Outage Performance Evaluation of Measured Vehicular Communication Radio Channels

[ES] Los sistemas cooperativos para entornos vehiculares tienen la capacidad de mejorar tanto la seguridad en carretera, como la gestión del tráfico. Tienen como base la norma del estándar de comunicaciones inalámbrico de red de área local (Wireless Local Area Network, WLAN) para el uso comunicaciones vehiculares (Vehicle-to-Vehicle/Infrastructure, V2I), denominada IEEE 802.11p, la cual se está desarrollando actualmente, y que dará lugar a la nueva tecnología de comunicaciones entre vehículos e infraestructura WAVE (Wireless Access in Vehicular Environments). Funcionando en el rango de frecuencias de 5.850 a 5.925 GHz, los sistemas WAVE adoptan la técnica de multiplexación OFDM (Orthogonal Frequency Division Multiplexing) y alcanzan tasas de transmisión de datos en el rango de 6 a 27 Mbps. El estudio del canal es clave para conocer el efecto de las condiciones de propagación reales sobre la transmisión. Habrá que tener en cuenta que en entornos de comunicaciones vehiculares se da la propagación con línea de visión directa (Line of Sight, LoS), por lo que a la hora de caracterizar el canal, habrá que considerar tanto el desvanecimiento Rayleigh como el desvanecimiento Ricean. Este estudio se hará a partir del procesado de la función de transferencia del canal obtenido para diferentes escenarios durante la campaña de medidas realizada en Lund, Suecia. en 2007 por la Universidad Técnica de Viena. El sistema radio utilizado considera múltiples antenas, es decir, el canal es Multiple-Input Multiple-Output (MIMO), dado que gracias a la diversidad consigue un mayor rendimiento. De cara a analizar el efecto de las condiciones de propagación sobre el rendimiento alcanzable, se caracterizará el canal mediante el Power Delay Profile (PDP) y el perfil de Path Loss. A continuación se estudiarán más en detalle los canales MIMO con desvanecimiento Ricean, cruciales para las comunicaciones Vehicle-to-Vehicle, (V2V). En estos canales hay una tasa de datos crítica (RCRIT) dependiente de una relación señal a ruido (Signal-to- Noise Ratio, SNR) bajo la cual la transmisión de datos con cero outage es posible, de manera que el canal se comporta como un canal con ruido aditivo gaussiano (Additive White Gaussian Noise, AWGN). Se analizará la tanto eficiencia espectral en términos de capacidad ergódica y como la probabilidad de outage del canal vehicular para diferentes valores de relación señal a ruido.[EN] Roadway-vehicle cooperative systems will lead to improve driving safety. These systems relay on a wireless local area network (WLAN) standard for automotive use, called IEEE 802.11p, which is under development in order to implement Wireless Access in Vehicular Environments (WAVE). Operating at 5.850¿5.925 GHz, WAVE systems adopt orthogonal frequency-division multiplexing (OFDM) and achieve data rates of 6¿27 Mbps. The development of efficient vehicle-to-vehicle (V2V) communications systems requires an understanding of the underlying radio propagation channels in order to analyze the real impact of real-world propagation conditions. Vehicular communication channels are non-stationary, because the conditions of the channel vary abruptly due to the speed of the vehicles. The studied wireless communication scenario is predominantly Line of Sight (LoS) propagation scenario, therefore Rayleigh fading and Ricean fading have to be considered for channel characterization. The reference data to be analyzed have been obtained from a channel sounding campaign carried out by the Vienna University of Technology in Lund, Sweden in 2007. The radio system used for this campaign is a multiple-input multiple-output (MIMO) system. Radio channel parameters such as the power delay profile and the path loss are going to be analyzed in order to study the impact of real-world propagation conditions. Reliability in Ricean MIMO channel is going to be more deeply characterized, as it is crucial for safety related V2V applications. In such channels, there is a SNR-dependent critical data rate (RCRIT) below which signaling with zero outage is possible, and hence the fading channel behaves like an AWGN channel. For the vehicular time variant channel spectral efficiency is going to be evaluated in terms of ergodic capacity and outage performance is also going to be studied by means of outage probability.Alonso Gómez, A. (2009). Spectral Efficiency and Outage Performance Evaluation of Measured Vehicular Communication Radio Channels. http://hdl.handle.net/10251/27442.Archivo delegad

Mobilidade de comunicações entre veículos e infraestrutura

Mestrado em Engenharia Electrónica e TelecomunicaçõesThe unique characteristics of VANETs, such as high mobility, dynamic topology and frequent loss of connectivity, turn the network selection scheme into a complex problem. In a crowded wireless environment that surrounds us, mainly in urban areas, there is a proliferation and superposition of multiple networks and technologies. Therefore, in order to guarantee connectivity in a transparent way for users, the presence of a connection manager capable of taking informed decisions is crutial. With the increase of mobile traffic, several initiatives have been performed for deploying free/low-cost Wi-Fi hotspots across the cities, in order to offload traffic from the cellular networks into more cost-effective networks. On the one hand, clients benefit from lower data prices, and on the other hand, operators may reduce the amount of cellular infrastructure deployed. Furthermore, users will certainly prefer to connect to a free source of Internet whenever it is available instead of paying for it. Since nodes in VANETs are vehicles, the perception of the surrounding networks is constantly changing, becoming unstable with speed. Therefore, the high mobility of nodes in VANETs jeopardizes the existing network selection mechanisms, which for the network election, are based on Received Signal Strength (RSS) to choose where to connect. Moreover, in a VANET environment, there are no mechanisms capable of taking into account V2V communication according to the WAVE/DSRC technology. Thereby, we propose a connection manager which considers the Wi-Fi networks, cellular networks and the WAVE/DSRC technology to provide connectivity to vehicles. This connection manager is capable of looking into relevant data that is available in VANET-equipped vehicles, increasing the dynamic of the decision process. VCM is a connection manager optimized to operate in VANET scenarios, which takes into account the vehicle speed and heading, the infrastructure position along with their availability and also the number of hops to reach the service provider, besides the link quality. The proposed connection manager is based on an Analytical Hierarchic Process (AHP) that combines several candidate networks, geographic inputs and physical factors to determine the best connection at all times, including the technology and the best network, for each user. To determine the priority of each parameter, we proposed the combination of pairwise comparisons between the criteria involved, according to Saaty's pairwise comparison scale, enhancing the process through simulation and using a Genetic Algorithm (GA). To observe the enhancements provided by VCM, two typical connection managers were implemented: BCM which only looks to the signal quality to choose where to connect, and PCM which takes into account users preference besides the RSS. The evaluation was performed in a Manhattan grid, composed by several vehicles using SUMO's car-following model and with equal turn probabilities, and infrastructure randomly spread across the scenario. The results show that VCM outperforms the other two connection managers, proving that it is capable of operating in general scenarios minimizing the packet loss and with a reduced number of performed handovers.As características únicas das redes veiculares, como a elevada mobilidade, a topologia dinâmica e a frequente perda de conectividade, tornam o esquema da escolha de rede num problema complexo. Num ambiente replecto de redes sem fios, principalmente nas áreas urbanas, existe um aglomerado e sobreposição de varias redes e tecnologias. Assim, para garantir ao utilizador a conectividade de forma transparente, é necessário a presença de um mecanismo capaz de tomar decisões informadas. Com o aumento do trafego móvel, varias iniciativas estão a ser realizadas, disponibilizando hotspots IEEE 802.11 a/g/n (Wi-Fi) pelas cidades, de forma a retirar trafego das redes celulares. Por um lado, os clientes podem usufruir de preços mais baixos e por outro lado, os operadores conseguem reduzir a quantidade de trafego móvel. Alem disso, os utilizadores irão preferir ligar-se a uma rede mais barata/grátis sempre que estiver disponível, desde que tenha boa qualidade. Uma vez que nas redes veiculares os nos são veículos, as redes disponíveis estão sempre a mudar, tornando-se cada vez mais instáveis com o aumento da velocidade. Assim, a mobilidade dos nos põe em causa as soluções existentes para mecanismos de selecção de redes, que maioritariamente para elegerem a melhor rede se baseiam apenas na qualidade do sinal. Alem disso, para um ambiente de redes veiculares, não existem mecanismos de selecção capazes de ter em conta comunicação Vehicle-to-Vehicle (V2V) de acordo com a tecnologia Wireless Access in Vehicular Environments (WAVE) / (Dedicated Short-Range Communications (DSRC). Assim, é proposta a criação de um gestor de conectividade capaz de ter em conta determinados factores que se encontram disponíveis nos veículos Vehicular Ad-hoc NETwork (VANET)-equipados para aumentar a dinâmica do processo de seleccao. O Vanet Connection Manager (VCM) é um gestor de conectividade optimizado para ambientes veiculares, que considera a disponibilidade de redes Wi-Fi, redes celulares e a tecnologia WAVE / DSRC para veículos. Este gestor tem em conta a velocidade e direcção do veículo, a posição das infraestructuras bem como a sua disponibilidade, o numero de saltos ate ao destino, alem da qualidade do sinal. O mecanismo proposto e baseado num Processo Analítico Hierárquico que combina varias redes candidatas, parâmetros geográficos e factores físicos para determinar a melhor ligação possível, incluindo a tecnologia e a melhor rede, para cada utilizador. Para o calculo das prioridades de cada parâmetro, foi proposto o método das combinações emparelhadas desenvolvido por Saaty, optimizando o processo através de simulação e recorrendo a um Algoritmo Genético. Para observar o desempenho do gestor de conectividade, implementaram-se dois gestores típicos de conectividade: Basic Connection Manager (BCM) que apenas tem em conta a força de sinal para escolher o melhor candidato, e o Preference-based Connection Manager (PCM) que tem em conta as preferências dos utilizadores para além da força de sinal. A avaliação foi realizada num cenário Manhattan, composto por vários veículos com modelos de simulação importados do SUMO e infraestrutura aleatoriamente colocada ao longo do cenário. Os resultados mostram que o VCM apresenta melhores resultados que os outros dois gestores de rede, provando que e capaz de operar em qualquer cenário, minimizando as perdas de dados e com um reduzido numero de mudanças de rede