{"title":"基于迭代TOPSIS的异构无线接入多属性网络选择","authors":"F. Bari, V. Leung","doi":"10.1109/CCNC.2007.164","DOIUrl":null,"url":null,"abstract":"Contemporary multimedia consumer devices are increasingly obtaining network connectivity mostly through wireless means. In order to economically support the mobile lifestyle of users, a new class of multimodal consumer devices has emerged that are equipped with heterogeneous wireless access capability. Inter-working of heterogeneous packet switched wireless networks, e.g., cellular and WLANs, via IP is a key step to provide ubiquitous service delivery via seamless connectivity of consumer devices. These wireless networks have a diverse range of capabilities and therefore selection of a specific network to optimize service delivery is an issue. Various algorithms have been proposed for use in the decision making process, with the class of Multi Attribute Decision Making (MADM) methods being one of the most promising. MADM methods, however, are known to suffer from ranking abnormalities. This paper applies TOPSIS, a MADM algorithm, to the problem of network selection. The causes of ranking abnormalities in TOPSIS are analyzed. An improvement to the algorithm as applied to the problem of network selection, where only the top ranking alternatives are considered important for decision making, is proposed. The new approach iteratively applies TOPSIS to the problem, removing the bottom ranked candidate network after each iteration. Simulation results are presented to demonstrate the effectiveness of the proposed iterative TOPSIS approach.","PeriodicalId":166361,"journal":{"name":"2007 4th IEEE Consumer Communications and Networking Conference","volume":"42 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"142","resultStr":"{\"title\":\"Multi-Attribute Network Selection by Iterative TOPSIS for Heterogeneous Wireless Access\",\"authors\":\"F. Bari, V. Leung\",\"doi\":\"10.1109/CCNC.2007.164\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Contemporary multimedia consumer devices are increasingly obtaining network connectivity mostly through wireless means. In order to economically support the mobile lifestyle of users, a new class of multimodal consumer devices has emerged that are equipped with heterogeneous wireless access capability. Inter-working of heterogeneous packet switched wireless networks, e.g., cellular and WLANs, via IP is a key step to provide ubiquitous service delivery via seamless connectivity of consumer devices. These wireless networks have a diverse range of capabilities and therefore selection of a specific network to optimize service delivery is an issue. Various algorithms have been proposed for use in the decision making process, with the class of Multi Attribute Decision Making (MADM) methods being one of the most promising. MADM methods, however, are known to suffer from ranking abnormalities. This paper applies TOPSIS, a MADM algorithm, to the problem of network selection. The causes of ranking abnormalities in TOPSIS are analyzed. An improvement to the algorithm as applied to the problem of network selection, where only the top ranking alternatives are considered important for decision making, is proposed. The new approach iteratively applies TOPSIS to the problem, removing the bottom ranked candidate network after each iteration. Simulation results are presented to demonstrate the effectiveness of the proposed iterative TOPSIS approach.\",\"PeriodicalId\":166361,\"journal\":{\"name\":\"2007 4th IEEE Consumer Communications and Networking Conference\",\"volume\":\"42 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"1900-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"142\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2007 4th IEEE Consumer Communications and Networking Conference\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/CCNC.2007.164\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2007 4th IEEE Consumer Communications and Networking Conference","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CCNC.2007.164","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Multi-Attribute Network Selection by Iterative TOPSIS for Heterogeneous Wireless Access
Contemporary multimedia consumer devices are increasingly obtaining network connectivity mostly through wireless means. In order to economically support the mobile lifestyle of users, a new class of multimodal consumer devices has emerged that are equipped with heterogeneous wireless access capability. Inter-working of heterogeneous packet switched wireless networks, e.g., cellular and WLANs, via IP is a key step to provide ubiquitous service delivery via seamless connectivity of consumer devices. These wireless networks have a diverse range of capabilities and therefore selection of a specific network to optimize service delivery is an issue. Various algorithms have been proposed for use in the decision making process, with the class of Multi Attribute Decision Making (MADM) methods being one of the most promising. MADM methods, however, are known to suffer from ranking abnormalities. This paper applies TOPSIS, a MADM algorithm, to the problem of network selection. The causes of ranking abnormalities in TOPSIS are analyzed. An improvement to the algorithm as applied to the problem of network selection, where only the top ranking alternatives are considered important for decision making, is proposed. The new approach iteratively applies TOPSIS to the problem, removing the bottom ranked candidate network after each iteration. Simulation results are presented to demonstrate the effectiveness of the proposed iterative TOPSIS approach.