scholarly journals Improving Roadside Unit Deployment in Vehicular Networks by Exploiting Genetic Algorithms

2018 ◽  
Vol 8 (1) ◽  
pp. 86 ◽  
Author(s):  
Manuel Fogue ◽  
Julio Sanguesa ◽  
Francisco Martinez ◽  
Johann Marquez-Barja
Keyword(s):  
Genetic Algorithms ◽  
Vehicular Networks ◽  
Roadside Unit

A Roadside Unit-Based Localization Scheme to Improve Positioning for Vehicular Networks

2016 ◽  
Author(s):  
Frances A. Santos ◽  
Ademar T. Akabane ◽  
Roberto S. Yokoyama ◽  
Antonio A. F. Loureiro ◽  
Leandro A. Villas
Keyword(s):  
Vehicular Networks ◽  
Localization Scheme ◽  
Roadside Unit

scholarly journals Adaptive Content Precaching Scheme Based on the Predictive Speed of Vehicles in Content-Centric Vehicular Networks

Sensors ◽  
2021 ◽  
Vol 21 (16) ◽  
pp. 5376
Author(s):  
Youngju Nam ◽  
Hyunseok Choi ◽  
Yongje Shin ◽  
Euisin Lee ◽  
Eun-Kyu Lee
Keyword(s):  
Vehicular Networks ◽  
Current Speed ◽  
Constant Speed ◽  
Multimedia Data ◽  
Average Speed ◽  
Large Size ◽  
Adaptive Value ◽  
Roadside Unit ◽  
Simulation Results ◽  
Adaptive Content

Content-Centric Vehicular Networks (CCVNs) are considered as an attractive technology to efficiently distribute and share contents among vehicles in vehicular environments. Due to the large size of contents such as multimedia data, it might be difficult for a vehicle to download the whole of a content within the coverage of its current RoadSide Unit (RSU). To address this issue, many studies exploit mobility-based content precaching in the next RSU on the trajectory of the vehicle. To calculate the amount of the content precaching, they use a constant speed such as the current speed of the vehicle requesting the content or the average speed of vehicles in the next RSU. However, since they do not appropriately reflect the practical speed of the vehicle in the next RSU, they could incorrectly calculate the amount of the content precaching. Therefore, we propose an adaptive content precaching scheme (ACPS) that correctly estimates the predictive speed of a requester vehicle to reflect its practical speed and calculates the amount of the content precaching using its predictive speed. ACPS adjusts the predictive speed to the average speed starting from the current speed with the optimized adaptive value. To compensate for a subtle error between the predictive and the practical speeds, ACPS appropriately adds a guardband area to the precaching amount. Simulation results verify that ACPS achieves better performance than previous schemes with the current or the average speeds in terms of the content download delay and the backhaul traffic overhead.

scholarly journals A Mobility Prediction-Based Relay Cluster Strategy for Content Delivery in Urban Vehicular Networks

2021 ◽  
Vol 11 (5) ◽  
pp. 2157
Author(s):  
Shaoqi Yue ◽  
Qi Zhu
Keyword(s):  
Vehicular Networks ◽  
Success Probability ◽  
Content Delivery ◽  
Statistical Characteristics ◽  
Trajectory Prediction ◽  
Urban Road ◽  
Cluster Strategy ◽  
Roadside Unit ◽  
Multiple Vehicles ◽  
High Dynamic

In recent years, cache-enabled vehicles have been introduced to improve the efficiency of content delivery in vehicular networks. However, because of the high dynamic of network topology, it is a big challenge to increase the success probability of content delivery. In this paper, we propose a relay strategy based on cluster’s prediction trajectory for the situation of no cache near the request vehicles. In our strategy, the roadside unit (RSU) divides vehicles into clusters by their prediction trajectory, and then proactively caches contents at a cluster that will be about to meet the request vehicle. In order to decrease the probability of unsuccessful content delivery caused by communication duration that is too short between the request vehicle and content source vehicle, RSU caches content chunks at multiple vehicles in a cluster. By letting the request vehicle communicate with vehicle-caching content chunks one by one, our strategy enlarges the communication duration and increases the success probability. Our strategy also maximizes the success probability by optimizing the number of vehicles selected to cache content chunks. Besides, based on statistical characteristics of vehicles’ speed, we derive the formula of success probability of content delivery. The simulation results show that our strategy can increase the success probability of content delivery, as well as decrease time delay, for example. For example, we increase the success probability by about 20%. Since the trajectory prediction-based cluster-dividing mechanism can improve clusters’ stability at intersections, this method is well suited for urban road scenarios.

Sign in / Sign up

Export Citation Format

Share Document