Analytical modeling of link duration for vehicular ad hoc networks in urban environment

International Journal of Distributed Sensor Networks

et al. 2016

Link duration between two vehicles is considered an important quality of service metric in designing a network protocol for vehicular networks. There exist many works that study the probability density functions of link duration in a vehicular network given various vehicle mobility models, for example, the random waypoint model. None of them, however, provides a practical solution to estimating the link duration between two vehicles on the road. This is in part because link duration between vehicles is affected by many factors including the distance between vehicles, their turning directions at intersections, and the impact of traffic lights. Considering these factors, we propose the extended link duration prediction (ELDP) model which allows a vehicle to accurately estimate how long it will be connected to another vehicle. The ELDP model does not assume that vehicles follow certain mobility models; instead, it assumes that a vehicle's velocity follows the Normal distribution. We validate the ELDP model in both highway and city scenarios in simulations. Our detailed simulations illustrate that relative speed between vehicles plays a vital role in accurately predicting link duration in a vehicular network. On the other hand, we find that the turning directions of a vehicle at intersections have subtle impact on the prediction results.

Section: Principle Of the Eldp Modelmentioning

confidence: 99%

“…A model for link duration between vehicles in one-dimensional highway scenarios is first proposed in [22]. More link duration models for city scenarios are then provided in [23][24][25]. In [26], we propose a link duration prediction (LDP) model that is suitable for both highway and city scenarios.…”

Section: Introductionmentioning

confidence: 99%

ELDP: Extended Link Duration Prediction Model for Vehicular Networks

International Journal of Distributed Sensor Networks

et al. 2016

“…Although some existing works considered the impact of traffic light, they simply assume the events of two consecutive vehicles encountering traffic lights combination are independent, which is different from the real situations [25]. Instead, in our LDP model, we assume these events are not independent and thus makes our model more practical.…”

Section: Impact Of Traffic Light On Link Duration Predictionmentioning

confidence: 99%

Practical Link Duration Prediction Model in Vehicular Ad Hoc Networks

International Journal of Distributed Sensor Networks

et al. 2015

Link duration prediction is one of the most fundamental problems in vehicular ad hoc networks (VANETs) as it determines the network performance of many vehicular applications. Existing analytical analysis about link duration in both mobile ad hoc network (MANETs) and VANETs is too complicated to be applied in a practical setting. Assuming vehicle's velocity follows the normal distribution, we propose a practical model which considers the distribution of relative velocity, intervehicle distance, and impact of traffic lights to estimate the expected link duration between any pair of connected vehicles. Such model is implemented on each vehicle along with (1) a relative velocity estimation approach and (2) an exponential moving average-(EMA-) based data processing procedure. Furthermore, the proposed model assumes that the events of two consecutive vehicles encountering traffic lights combination are dependent, which make the model more practical. Simulation results show that the link duration model predicts link duration with the average accuracy of 10% and 20% in highway and city scenarios, respectively.

“…First, it can be applied to city scenarios and presents scheduling algorithms that optimize network performance. It is based on the link duration estimation work, such as [6] and [13] These works considered several factors that affect the link duration time between two vehicles. These factors include vehicle velocity, driving direction, street map data and traffic light states.…”

Section: Related Literaturementioning

confidence: 99%

“…Reference [6] and [13] have shown that, the link duration time can be estimated according to the vehicle velocity, the map, the driving direction, and the traffic light period. So it is possible to calculate for each node.…”

Section: Further Detailsmentioning

confidence: 99%

Schedule Algorithms for File Transmission in Vehicular Ad Hoc Networks

Larson

Wireless Algorithms, Systems, and Applications

et al. 2014

Abstract. The development of vehicular ad-hoc networks has lead to several new services, such as instant messages, multi-media videos and large data transmissions. These applications have altered the pattern of data transmissions on VANETs to include larger volumes of data with different file sizes. Unstable links in vehicular networks are common due to channel fading and because of the vehicle moving, file transmissions are vulnerable to link interruption. How to utilize the limited link duration to transfer more files of different sizes successfully is a significant problem. Currently, the common practice to schedule file transmissions is a First In First Out (FIFO) mechanism defined in the DSRC (Dedicated Short Range Communication) standard. The algorithms presented in this paper show significant performance improvements. We proposed two algorithms, one based on transmission times and one based on due times. Both algorithms solve this problem by dynamically scheduling the file transmitting sequence according to selected criteria. Simulation results show that our algorithms significantly improve performance compared with FIFO mechanism for the transmission success rate and the network throughput.