Real-world evaluation of IEEE 802.11p for vehicular networks

Neves, Filipe; Cardote, André; Moreira, Ricardo; Sargento, Susana

doi:10.1145/2030698.2030717

Cited by 13 publications

(7 citation statements)

References 2 publications

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“…For such communicating pairs, multiple interaction reflections and scattering are the dominant contributors of power at the receiver [30]; calculating such rays incurs prohibitively high computations and a geographical database with a high level of detail. Furthermore, our measurement results and those reported in similar studies (e.g., [6], [24], [25]) show that communication range in NLOSb (i.e., building-obstructed) conditions using DSRC radios operating in the 5.9 GHz frequency band is limited to approximately 200 meters, even with the maximum transmit power allowed by the standard [41]. Thus, in order to avoid costly geometric computations which predominantly yield power levels below reception threshold, at the same time allowing for communication in deeply faded areas, we determine the received power as follows.…”

Section: E Practical Considerations For Different Link Types and Prosupporting

confidence: 91%

“…Furthermore, we use the communication ranges specified in Table VI. These values are based on our own measurements, as well as results previously collected in [6], [7], [24], [25]. Note that r LOS was set to 1000 meters outside urban areas and 500 meters in urban areas, whereas we use the same values of r NLOSv and r NLOSb in all environments.…”

Section: Resultsmentioning

confidence: 99%

“…Furthermore, the 2-D nature of the roads in suburban and urban areas implies that communication also happens outside the road surface. In such cases, static obstructions such as buildings and foliage play an important role [24], [25]. Buildings and foliage are the main source of obstructions for communication on the intersections and across different streets [7], whereas buildings and vehicles are the main sources of reflections and diffractions [26].…”

Section: Description Of the Channel Modelmentioning

confidence: 99%

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Geometry-Based Vehicle-to-Vehicle Channel Modeling for Large-Scale Simulation

Boban¹,

Barros

Tonguz

2014

IEEE Trans. Veh. Technol.

208

145

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Large-scale Vehicular Ad Hoc Network (VANET) simulators by and large employ simple statistical channel models. By design, such models do not account for specific objects in the region of interest when estimating the channel. While computationally efficient, these models were shown to be unable to provide satisfactory accuracy on a link level for typical VANET scenarios. Specifically, experimental studies have shown that both large static objects (e.g., buildings and foliage) as well as mobile objects (surrounding vehicles) have a profound impact on the quality of vehicle-to-vehicle (V2V) channels. While several recently proposed large-scale V2V channel models account for static objects (e.g., buildings) in the simulated area, there is a need for a comprehensive model that takes into account both the static and the mobile objects. To fill this gap, we designed a geometry-based, computationally manageable V2V channel model that uses the real-world locations and the actual dimensions of vehicles, buildings, and foliage to simulate the V2V channel more realistically. We use the outlines of the modeled objects to form spatial tree structures for efficient manipulation of geographic data. We distinguish and model separately the following three link types: line of sight (LOS), non-LOS due to vehicles, and non-LOS due to static objects. Apart from the model for large-scale signal variations, we also propose a simple model for small-scale signal variation using the number and size of the objects around the communicating vehicles. We validate the models against extensive measurements performed in urban, suburban, highway, and open space environment. We provide the complete simulation recipe for the implementation of the model in simulators. Finally, we implement the model in Matlab and show that it scales well by simulating networks with tens of thousands of objects and hundred thousand communicating vehicle pairs using commodity hardware.

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Section: E Practical Considerations For Different Link Types and Prosupporting

confidence: 91%

Section: Resultsmentioning

confidence: 99%

Section: Description Of the Channel Modelmentioning

confidence: 99%

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Geometry-Based Vehicle-to-Vehicle Channel Modeling for Large-Scale Simulation

Boban¹,

Barros

Tonguz

2014

IEEE Trans. Veh. Technol.

208

145

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“…Despite the great potential of VANETs, current researches are mainly based on simulations or in the development of routing protocols, reinforcing the need for practical experiments [17]. The explanation lies in the scarcity of vehicle communication devices and their high cost, besides the high complexity involved during real experiments.…”

Section: Introductionmentioning

confidence: 99%

Vehicle monitoring system using IEEE 802.11p device and Android application

Barcelos

Amarante

Drury

et al. 2014

2014 IEEE Symposium on Computers and Communications (ISCC)

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In Vehicular Ad hoc Networks (VANETs), data can be transmitted between vehicles and infrastructured devices. This paper proposes the development of a communication device capable of operating in both IEEE 802.11p and 3G modes. To validate the operation of the communication device, Android and server applications were developed, in order to collect vehicle physical data (such as engine status, speed and GPS location) and transmit them to a server, using the communication device proposed. The device was able to transmit vehicle status to the server successfully, choosing the best available link between 3G and IEEE 802.11p.

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“…Beside the power, loss-rate, throughput, and delivery ratio measurements, they presented their methodology for conducting experiments with 'real-world' mobile ad hoc networks and discussed problems of beacon position-based routing due to radio fluctuations. Neves et al [NCMS11] developed a small-scale testbed for experimenting the IEEE 802.11p protocol [IEE10] in a real-world scenario. This testbed consisted on two on-board units (single computer), two wireless interfaces, two antennas, and a GPS.…”

Section: Real-world Testbedsmentioning

confidence: 99%

Using Ontologies and Intelligent Systems for Traffic Accident Assistance in Vehicular Environments

Villalba¹

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To my parents, my sister, and my girlfriend,for their patience and support. AcknowledgmentsSince I was a child I had always wanted to get a university education when I grew up. At that time, I had different studies on my mind; I wanted to be anything from a lawyer to an architect (dreams and ideas of children). I was focusing my preferences towards engineering and at the end I decided to combine it with one of my passions: computers. Just as I finished my Computer Science Degree, in 2011, one of my advisors, Dr. Piedad Garrido, offered me the chance to work with her as a researcher. Since finding a job was not easy at that moment due to the global economic recession, especially in my town, Teruel, I immediately took her up on the offer. At first, I worked with her and Dr. Francisco José Serón on issues related to Artificial Intelligence in Computer Graphics. After a while, Piedad and, Dr. Francisco José Martínez, encouraged me to continue my academic training. So, I began to study my Master's Degree in Artificial Intelligence, with the aim of carrying on with the development of this Thesis. In addition, I changed my scope in order to combine artificial intelligence with vehicular networks in my research. All this time they have given me the support and the motivation I have needed, as well as critiquing and correcting my work. Thus, I would like to give them my sincere thanks; without them, I would never have achieved this goal.After having gone on this adventure, I had the opportunity to meet the people from the Grupo de Redes de Computadores (GRC) at the Universitat Politècnica de València: Dr. Carlos Tavares Calafate, Dr. Pietro Manzoni, and especially, Dr. Juan Carlos Cano, my other advisor. I wish to thank all them for giving me the chance to work together with them; their feedback and corrections have both helped me to improve my research world. It has been an enriching experience.I will not forget my classmates of the Intelligent Networks and Information Technologies (iNiT) research group of the University of Zaragoza, Manolo, Fernando, Vicente, Jesús Ibáñez, Chon, Jesús Fuentes, and Julio, our hard work has made its existence possible. Besides, our conversations and discussions about any daily topic, as real experts, have served as release when we were under a lot of pressure. Also, I cannot miss out my other university peers,Ángel and Nava, who made the coffee breaks more enjoyable. Thank you! I do not want to forget Dr. Severino Fernández, my Master's Thesis advisor whose proposals and corrections related to Evolutionary Algorithms were very useful for me.I am also very thankful to the Fundación Antonio Gargallo, the Universitat v Politècnica de València, and the Diputación Provincial de Teruel for their economic support for the development of this work. My loving thanks to my father Javier and my mother Mercedes for the sacrifices that they have made in order to give me the opportunity to have come so far, the upbringing that they have given me, the moral values that they have taught me, and their unc...

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Real-world evaluation of IEEE 802.11p for vehicular networks

Cited by 13 publications

References 2 publications

Geometry-Based Vehicle-to-Vehicle Channel Modeling for Large-Scale Simulation

Geometry-Based Vehicle-to-Vehicle Channel Modeling for Large-Scale Simulation

Vehicle monitoring system using IEEE 802.11p device and Android application

Using Ontologies and Intelligent Systems for Traffic Accident Assistance in Vehicular Environments

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