{"title":"基于变化树的传感器网络流优化","authors":"Albert Williams, D. Towsley","doi":"10.1109/MILCOM52596.2021.9653006","DOIUrl":null,"url":null,"abstract":"Military sensor networks often operate in resource challenged environments. This poses the problem of how to allocate resources to sensors flow to accomplish a mission. In this paper we consider a set of sensors that communicate observations up a tree to a fusion center. The value of the mission is modeled by a separable increasing concave functions and we develop a low complexity one step algorithm that allocates link capacities to each sensor so as to maximize this function. By limiting ourselves to a tree topology, we derive several important benefits, including the ability to quickly adapt to changes in utility functions or topology, and in a straightforward way to run our algorithm in a parallel, distributed manner over the network with little communication overhead and no centralized planning.","PeriodicalId":187645,"journal":{"name":"MILCOM 2021 - 2021 IEEE Military Communications Conference (MILCOM)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2021-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Optimizing Flows in Changing Tree-based Sensor Networks\",\"authors\":\"Albert Williams, D. Towsley\",\"doi\":\"10.1109/MILCOM52596.2021.9653006\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Military sensor networks often operate in resource challenged environments. This poses the problem of how to allocate resources to sensors flow to accomplish a mission. In this paper we consider a set of sensors that communicate observations up a tree to a fusion center. The value of the mission is modeled by a separable increasing concave functions and we develop a low complexity one step algorithm that allocates link capacities to each sensor so as to maximize this function. By limiting ourselves to a tree topology, we derive several important benefits, including the ability to quickly adapt to changes in utility functions or topology, and in a straightforward way to run our algorithm in a parallel, distributed manner over the network with little communication overhead and no centralized planning.\",\"PeriodicalId\":187645,\"journal\":{\"name\":\"MILCOM 2021 - 2021 IEEE Military Communications Conference (MILCOM)\",\"volume\":\"13 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2021-11-29\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"MILCOM 2021 - 2021 IEEE Military Communications Conference (MILCOM)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/MILCOM52596.2021.9653006\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"MILCOM 2021 - 2021 IEEE Military Communications Conference (MILCOM)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/MILCOM52596.2021.9653006","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Optimizing Flows in Changing Tree-based Sensor Networks
Military sensor networks often operate in resource challenged environments. This poses the problem of how to allocate resources to sensors flow to accomplish a mission. In this paper we consider a set of sensors that communicate observations up a tree to a fusion center. The value of the mission is modeled by a separable increasing concave functions and we develop a low complexity one step algorithm that allocates link capacities to each sensor so as to maximize this function. By limiting ourselves to a tree topology, we derive several important benefits, including the ability to quickly adapt to changes in utility functions or topology, and in a straightforward way to run our algorithm in a parallel, distributed manner over the network with little communication overhead and no centralized planning.