Lessons learned from a real vehicular network deployment of delay-tolerant networking

Monteiro, Romeu; Guedes, Luis; Condeixa, Tiago; Neves, Filipe; Sargento, Susana; Guardalben, Lucas; Steenkiste, Peter

doi:10.1109/iccw.2015.7247550

Cited by 5 publications

(2 citation statements)

References 6 publications

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“…Vehicles can take multiple routes to move among different locations. To discover the trajectories, we explored a rich vehicle dataset collected from a real-world VANET testbed deployed in the city of Porto, Portugal [ 31 , 34 ]. The testbed consists of 600 OBUs and 120 RSUs.…”

Section: Safety Message Dissemination With Vehicle Trajectory Predictionmentioning

confidence: 99%

Effective Safety Message Dissemination with Vehicle Trajectory Predictions in V2X Networks

Liu

Zhao

et al. 2022

Sensors

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Exploring data connection information from vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communications using advanced machine learning approaches, an intelligent transportation system (ITS) can provide better safety services to mitigate the risk of road accidents and improve traffic efficiency. In this work, we propose an end-edge-cloud architecture to deploy machine learning-driven approaches at network edges to predict vehicles’ future trajectories, which is further utilized to provide an effective safety message dissemination scheme. With our approach, the traffic safety message will only be disseminated to relevant vehicles that are predicted to pass by accident areas, which can significantly reduce the network data transmission overhead and avoid unnecessary interference. Depending on the vehicle connectivity, our system adaptively chooses vehicle-to-vehicle (V2V) or vehicle-to-infrastructure (V2I) communications to disseminate safety messages. We evaluate the system by using a real-world VANET mobility dataset, and experimental results show that our system outperforms other mechanisms without considering any predicted vehicle trajectory density information.

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Section: Safety Message Dissemination With Vehicle Trajectory Predictionmentioning

confidence: 99%

Effective Safety Message Dissemination with Vehicle Trajectory Predictions in V2X Networks

Liu

Zhao

et al. 2022

Sensors

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“…It is expected that in the future, all types of vehicles, such as cars, buses and trucks, will have the ability to form a network of connected vehicles. A practical example of this near future is a global-level vehicular communication platform which comprises more than 600 vehicles (taxis, trucks and buses) and fixed stations located in the city of Porto, Portugal [1][2][3].…”

Section: Introductionmentioning

confidence: 99%

Evaluation of Content Dissemination Strategies in Urban Vehicular Networks

et al. 2020

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The main drivers for the continuous development of Vehicular ad-hoc Networks (VANETs) are safety applications and services. However, in recent years, new interests have emerged regarding the introduction of new applications and services for non-urgent content (e.g., videos, ads, sensing and touristic information) dissemination. However, there is a lack of real studies considering content dissemination strategies to understand when and to whom the content should be disseminated using real vehicular traces gathered from real vehicular networks. This work presents a realistic study of strategies for dissemination of non-urgent content with the main goal of improving content delivery as well as minimizing network congestion and resource usage. First, we perform an exhaustive network characterization. Then, several content strategies are specified and evaluated in different scenarios (city center and parking lot). All the obtained results show that there are two content distribution strategies that clearly set themselves apart due to their superior performance: Local Rarest Bundle First and Local Rarest Generation First.

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