{"title":"Mobility-Based Clustering With Link Quality Estimation for Urban Vanets","authors":"H. Ferng, Muhammad Abdullah","doi":"10.1109/ICMLC48188.2019.8949241","DOIUrl":null,"url":null,"abstract":"Owing to high mobility and a large amount of vehicles in a vehicular ad hoc network (VANET), it is challenging to overcome the issues of frequent topology changes and network scalability. To mitigate these issues, a vehicle clustering and management scheme can be applied to VANETs. Towards this goal, a mobility-based clustering scheme with a clustering link quality estimation (CLQE) metric considering both mobility information and link quality estimation is proposed in this paper. The proposed clustering scheme is evaluated through the NS-3 simulator and our simulation results show that our proposed scheme outperforms the closely related schemes in most scenarios.","PeriodicalId":221349,"journal":{"name":"2019 International Conference on Machine Learning and Cybernetics (ICMLC)","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2019 International Conference on Machine Learning and Cybernetics (ICMLC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICMLC48188.2019.8949241","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 3
Abstract
Owing to high mobility and a large amount of vehicles in a vehicular ad hoc network (VANET), it is challenging to overcome the issues of frequent topology changes and network scalability. To mitigate these issues, a vehicle clustering and management scheme can be applied to VANETs. Towards this goal, a mobility-based clustering scheme with a clustering link quality estimation (CLQE) metric considering both mobility information and link quality estimation is proposed in this paper. The proposed clustering scheme is evaluated through the NS-3 simulator and our simulation results show that our proposed scheme outperforms the closely related schemes in most scenarios.