{"title":"Improving Shared Awareness and QoS Factors in AntNet Algorithm Using Fuzzy Reinforcement and Traffic Sensing","authors":"Pooia Lalbakhsh, Bahram Zaeri, M. Fesharaki","doi":"10.1109/ICFCC.2009.8","DOIUrl":null,"url":null,"abstract":"The paper Describes a novel method to introduce new concepts in functional and conceptual dimensions of routing algorithms in swarm-based communication networks.The method uses a fuzzy reinforcement factor in the learning phase of the system and a dynamic traffic monitor to analyze and control the changing network conditions.The combination of the mentioned approaches not only improves the routing process, it also introduces new ideas to face some of the swarm challenges such as dynamism and uncertainty by fuzzy capabilities. Our strategy is simulated on AntNet routing algorithm to produce the performance evaluation results. The proposed strategy is compared with the Standard AntNet to analyze instantaneous/average throughput and packet delay together with the network awareness capability. The presented results demonstrate the improved performance of our strategy against the standard algorithm.","PeriodicalId":338489,"journal":{"name":"2009 International Conference on Future Computer and Communication","volume":"32 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2009-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"8","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2009 International Conference on Future Computer and Communication","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICFCC.2009.8","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 8
Abstract
The paper Describes a novel method to introduce new concepts in functional and conceptual dimensions of routing algorithms in swarm-based communication networks.The method uses a fuzzy reinforcement factor in the learning phase of the system and a dynamic traffic monitor to analyze and control the changing network conditions.The combination of the mentioned approaches not only improves the routing process, it also introduces new ideas to face some of the swarm challenges such as dynamism and uncertainty by fuzzy capabilities. Our strategy is simulated on AntNet routing algorithm to produce the performance evaluation results. The proposed strategy is compared with the Standard AntNet to analyze instantaneous/average throughput and packet delay together with the network awareness capability. The presented results demonstrate the improved performance of our strategy against the standard algorithm.