{"title":"Ant agents for hybrid multipath routing in mobile ad hoc networks","authors":"F. Ducatelle, G. D. Caro, L. Gambardella","doi":"10.1109/WONS.2005.3","DOIUrl":null,"url":null,"abstract":"In this paper we describe AntHocNet, an algorithm for routing in mobile ad hoc networks based on ideas from the nature-inspired ant colony optimization framework. The algorithm consists of both reactive and proactive components. In a reactive path setup phase, multiple paths are built between the source and destination of a data session. Data are stochastically spread over the different paths, according to their estimated quality. During the course of the session, paths are continuously monitored and improved in a proactive way. Link failures are dealt with locally. The algorithm makes extensive use of ant-like mobile agents which sample full paths between source and destination nodes in a Monte Carlo fashion. We report results of simulation experiments in which we have studied the behavior of AntHocNet and AODV as a function of node mobility, terrain size and number of nodes. According to the observed results, AntHocNet outperforms AODV both in terms of end-to-end delay and delivery ratio.","PeriodicalId":120653,"journal":{"name":"Second Annual Conference on Wireless On-demand Network Systems and Services","volume":"15 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2005-01-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"109","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Second Annual Conference on Wireless On-demand Network Systems and Services","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/WONS.2005.3","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 109
Abstract
In this paper we describe AntHocNet, an algorithm for routing in mobile ad hoc networks based on ideas from the nature-inspired ant colony optimization framework. The algorithm consists of both reactive and proactive components. In a reactive path setup phase, multiple paths are built between the source and destination of a data session. Data are stochastically spread over the different paths, according to their estimated quality. During the course of the session, paths are continuously monitored and improved in a proactive way. Link failures are dealt with locally. The algorithm makes extensive use of ant-like mobile agents which sample full paths between source and destination nodes in a Monte Carlo fashion. We report results of simulation experiments in which we have studied the behavior of AntHocNet and AODV as a function of node mobility, terrain size and number of nodes. According to the observed results, AntHocNet outperforms AODV both in terms of end-to-end delay and delivery ratio.