Paper Info
Title
Driving Path Stability in VANETs.
Abstract
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.
Year
DOI
Venue
2018
10.1109/GLOCOM.2018.8647450
IEEE Global Communications Conference
Keywords
Field
DocType
VANET,Stability of communication path,SVR
Lagrange polynomial,Path stability,Link-state routing protocol,Computer science,Word error rate,Support vector machine,Information security,Real-time computing,Vehicular ad hoc network,Routing protocol,Distributed computing
Conference
ISSN
Citations 
PageRank 
2334-0983
0
0.34
References 
Authors
0
6
Name
Order
Citations
PageRank
Mohammed Laroui100.68
Sellami, A.262.51
Boubakr Nour35112.78
Hassine Moungla46722.76
Hossam Afifi542869.12
Sofiane Boukli Hacene693.91