{"title":"移动自组织网络中混合多路径路由的蚂蚁代理","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":"{\"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}","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}
Ant agents for hybrid multipath routing in mobile ad hoc networks
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.