{"title":"基于Remora优化算法的优化节点聚类技术在VANETs中实现可靠的数据传输","authors":"Swathi Konduru , M. Sathya","doi":"10.1016/j.ijin.2022.07.002","DOIUrl":null,"url":null,"abstract":"<div><p>Vehicular ad hoc Network (VANET) is one of the recently growing trends which motivate the provision of several service providers in the urban areas. In VANETs, the vehicles represent the nodes in the network topology that needs to guarantee better cooperation when there is a higher node density. Moreover, the problem of determining an optimal route and achieving network scalability is a herculean task. In this context, the incorporation of a potential clustering algorithm has the possibility of improving the road safety and facilitating a reliable option of promoting message routing. The clustering protocols are determined to be the ideal candidate for solving the problems of network scalability to guarantee reliable data dissemination. In this paper, Remora optimization algorithm-based Optimized Node Clustering (ROAONC) Technique is proposed for node clustering in VANETs to achieved optimal CH selection process. This ROAONC scheme was proposed for minimizing network overhead in the scenarios of unpredictable node density. The simulation results of this ROAONC scheme confirmed better performance in terms of transmission range, node density, network area and number of clusters compared to the competitive ant colony, grey wolf, grasshopper, and dragonfly optimization algorithm-based clustering protocols.</p></div>","PeriodicalId":100702,"journal":{"name":"International Journal of Intelligent Networks","volume":"3 ","pages":"Pages 74-79"},"PeriodicalIF":0.0000,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666603022000082/pdfft?md5=1c34e7af1d5a3879e53b71b071d8e46a&pid=1-s2.0-S2666603022000082-main.pdf","citationCount":"2","resultStr":"{\"title\":\"Remora optimization algorithm-based optimized node clustering technique for reliable data delivery in VANETs\",\"authors\":\"Swathi Konduru , M. Sathya\",\"doi\":\"10.1016/j.ijin.2022.07.002\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>Vehicular ad hoc Network (VANET) is one of the recently growing trends which motivate the provision of several service providers in the urban areas. In VANETs, the vehicles represent the nodes in the network topology that needs to guarantee better cooperation when there is a higher node density. Moreover, the problem of determining an optimal route and achieving network scalability is a herculean task. In this context, the incorporation of a potential clustering algorithm has the possibility of improving the road safety and facilitating a reliable option of promoting message routing. The clustering protocols are determined to be the ideal candidate for solving the problems of network scalability to guarantee reliable data dissemination. In this paper, Remora optimization algorithm-based Optimized Node Clustering (ROAONC) Technique is proposed for node clustering in VANETs to achieved optimal CH selection process. This ROAONC scheme was proposed for minimizing network overhead in the scenarios of unpredictable node density. The simulation results of this ROAONC scheme confirmed better performance in terms of transmission range, node density, network area and number of clusters compared to the competitive ant colony, grey wolf, grasshopper, and dragonfly optimization algorithm-based clustering protocols.</p></div>\",\"PeriodicalId\":100702,\"journal\":{\"name\":\"International Journal of Intelligent Networks\",\"volume\":\"3 \",\"pages\":\"Pages 74-79\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2022-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.sciencedirect.com/science/article/pii/S2666603022000082/pdfft?md5=1c34e7af1d5a3879e53b71b071d8e46a&pid=1-s2.0-S2666603022000082-main.pdf\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"International Journal of Intelligent Networks\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S2666603022000082\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal of Intelligent Networks","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2666603022000082","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Remora optimization algorithm-based optimized node clustering technique for reliable data delivery in VANETs
Vehicular ad hoc Network (VANET) is one of the recently growing trends which motivate the provision of several service providers in the urban areas. In VANETs, the vehicles represent the nodes in the network topology that needs to guarantee better cooperation when there is a higher node density. Moreover, the problem of determining an optimal route and achieving network scalability is a herculean task. In this context, the incorporation of a potential clustering algorithm has the possibility of improving the road safety and facilitating a reliable option of promoting message routing. The clustering protocols are determined to be the ideal candidate for solving the problems of network scalability to guarantee reliable data dissemination. In this paper, Remora optimization algorithm-based Optimized Node Clustering (ROAONC) Technique is proposed for node clustering in VANETs to achieved optimal CH selection process. This ROAONC scheme was proposed for minimizing network overhead in the scenarios of unpredictable node density. The simulation results of this ROAONC scheme confirmed better performance in terms of transmission range, node density, network area and number of clusters compared to the competitive ant colony, grey wolf, grasshopper, and dragonfly optimization algorithm-based clustering protocols.