{"title":"A new adaptive routing approach based on Ant Colony Optimization (ACO) for Ad hoc Wireless Networks","authors":"N. Chowdhury, S. M. Baker, E. H. Choudhury","doi":"10.1109/ICCITECHN.2008.4803126","DOIUrl":null,"url":null,"abstract":"The goal of this work is to design a new adaptive routing technique for ad hoc wireless networks. This paper proposed the basic deign of the algorithm that works based on the principle of ant colony optimization (ACO). This is a probabilistic adaptive technique that changes its routes with the change of network topology over the period of time by learning its environment. It identifies appropriate paths with the feedback of previously travelled packets and maintains routing table accordingly. A self-made simulator implemented on C++ is used to evaluate performance of this algorithm on the basis of diverse adaptive issues such as change of probability, growth of pheromone intensity, randomness of the selection and packet sending rate through different paths.","PeriodicalId":335795,"journal":{"name":"2008 11th International Conference on Computer and Information Technology","volume":"22 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2008-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"7","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2008 11th International Conference on Computer and Information Technology","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICCITECHN.2008.4803126","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 7
Abstract
The goal of this work is to design a new adaptive routing technique for ad hoc wireless networks. This paper proposed the basic deign of the algorithm that works based on the principle of ant colony optimization (ACO). This is a probabilistic adaptive technique that changes its routes with the change of network topology over the period of time by learning its environment. It identifies appropriate paths with the feedback of previously travelled packets and maintains routing table accordingly. A self-made simulator implemented on C++ is used to evaluate performance of this algorithm on the basis of diverse adaptive issues such as change of probability, growth of pheromone intensity, randomness of the selection and packet sending rate through different paths.