Probability distribution of link life time in vehicular ad hoc networks

Shelly, Siddharth; Babu, A. V.

doi:10.1109/icacci.2013.6637387

Cited by 8 publications

(5 citation statements)

References 21 publications

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“…Shelly et al [2] proposed a statistic method for link lifetime in Vanet networks by using an analytical model. They studied the impact of vehicle density, vehicle mobility, and the transmission range and analyzed the statistics of the communication link.…”

Section: A Improving Communications Using Predictors In Vehicular Net...mentioning

confidence: 99%

Prefetching of mobile devices information - a DNS perspective

Bernard¹,

Laroui

Marot

et al. 2022

ICC 2022 - IEEE International Conference on Communications

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HAL is a multi-disciplinary open access archive for the deposit and dissemination of scientific research documents, whether they are published or not. The documents may come from teaching and research institutions in France or abroad, or from public or private research centers. L'archive ouverte pluridisciplinaire HAL, est destinée au dépôt et à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

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Section: A Improving Communications Using Predictors In Vehicular Net...mentioning

confidence: 99%

Prefetching of mobile devices information - a DNS perspective

Bernard¹,

Laroui

Marot

et al. 2022

ICC 2022 - IEEE International Conference on Communications

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“…Authors in [16] studied the link life-time in VANETs using analytical model for Probability Density Function (PDF), they analyzed the statistics of link life time and the impact of transmission range, vehicle mobility, and vehicle density; by considering the scenario of communication is un-congested free flow traffic, where the state of movement of vehicles is independent. Authors were interested in the movement of two vehicles (A) and (B), with V A , V B and V r are velocities of vehicle A, velocities of vehicle B respectively, and the relative velocities between pair of vehicles in the network…”

Section: Related Workmentioning

confidence: 99%

Driving Path Stability in VANETs

Laroui¹,

Sellami²,

Nour³

et al. 2018

2018 IEEE Global Communications Conference (GLOBECOM)

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Vehicular Ad Hoc Network has attracted both research and industrial community due to its benefits in facilitating human life and enhancing the security and comfort. However, various issues have been faced in such networks such as information security, routing reliability, dynamic high mobility of vehicles, that influence the stability of communication. To overcome this issue, it is necessary to increase the routing protocols performances, by keeping only the stable path during the communication. The effective solutions that have been investigated in the literature are based on the link prediction to avoid broken links. In this paper, we propose a new solution based on machine learning concept for link prediction, using LR and Support Vector Regression (SVR) which is a variant of the Support Vector Machine (SVM) algorithm. SVR allows predicting the movements of the vehicles in the network which gives us a decision for the link state at a future time. We study the performance of SVR by comparing the generated prediction values against real movement traces of different vehicles in various mobility scenarios, and to show the effectiveness of the proposed method, we calculate the error rate. Finally, we compare this new SVR method with Lagrange interpolation solution.Index Terms-VANET, Stability of communication path, SVRMr. Laroui is corresponding author.Various routing protocols that can be used in wireless networks are proposed in the literature [8]. VANET-based solutions are expected to furnish methodical and proven solutions for innovative, and resource-efficient as in other wireless communication protocol [9]. These protocols can broadly be classified into three main categories: the first category is the proactive routing protocols, which aim to construct the routing tables before the request is made. A proactive routing protocol identifies the topology of the network at all times, for example, Destination Sequenced Distance Vector routing (DSDV). The second category is the reactive routing protocols, that consist of building a routing table only when a node receives a request. Protocols under this umbrella do not know the network topology; they determine the path to access a node of the network due to the demand of request, for example, Ad hoc On-Demand Distance Vector (AODV). Finally, the last category is the hybrid routing protocols. A hybrid protocol combines the two previously discussed categories: proactive and reactive concept. It uses a proactive protocol to get information about the nearest neighbors (maximum neighbors with two jumps). Beyond this predefined area, the hybrid protocol uses reactive protocol techniques to search for routes. This type of protocol adapts well to large networks.The main characteristics of VANET networks are the high mobility of vehicles, where each vehicle has a range of communication to provide communication directly with the destination vehicle if they are in the same range. Otherwise, a multi-hop communication needs to be established to allow communication with the destin...

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“…In [18,19], it is found that an exponential distribution is a good approximation of the link duration in VANETs. However, [20] presents a different result that the probability density function (pdf) of link duration in a VANET is close to the log-Normal function. Yan and Olariu [21] also show that the link in VANETs is a log-Normal function given the following two assumptions: (1) the pdf of intervehicle distance is a log-Normal function and (2) a vehicle's velocity is deterministic.…”

Section: Related Workmentioning

confidence: 99%

ELDP: Extended Link Duration Prediction Model for Vehicular Networks

Wang

Chen

et al. 2016

International Journal of Distributed Sensor Networks

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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.

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Probability distribution of link life time in vehicular ad hoc networks

Cited by 8 publications

References 21 publications

Prefetching of mobile devices information - a DNS perspective

Prefetching of mobile devices information - a DNS perspective

Driving Path Stability in VANETs

ELDP: Extended Link Duration Prediction Model for Vehicular Networks

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