Identifying effective sink node combinations in spacecraft data transfer networks

Author:

Clark Ruaridh A.,McGrath Ciara N.,Macdonald Malcolm

Abstract

AbstractComplex networks are emerging in low-Earth-orbit as the communication architectures of inter-linked space systems. These data transfer networks vary based on spacecraft interaction with targets and ground stations, which respectively represent source and sink nodes for data flowing through the network. We demonstrate how networks can be used to identify effective sink node selections that in combination provide source coverage, high data throughput, and low latency connections for intermittently connected, store-and-forward space systems. The challenge in this work is to account for the changing data transfer network that varies significantly depending on the ground stations selected—given a system where data is downlinked by spacecraft at the first opportunity. Therefore, passed-on networks are created to capture the redistribution of data following a sink node’s removal from the system, a problem of relevance to traffic management in a variety of flow network applications. Modelling the system using consensus dynamics, enables sink node selections to be evaluated in terms of their source coverage and data throughput. While restrictions in the depth of propagation when defining passed-on networks, ensures the optimisation implicitly rewards lower latency connections. This is a beneficial by-product for both space system design and store-and-forward data networks in general. The passed-on networks also provide an insight into the relationship between sink nodes, with eigenvector embedding-based communities identifying sink node divisions that correspond with differences in source node coverage.

Publisher

Springer Science and Business Media LLC

Subject

Computational Mathematics,Computer Networks and Communications,Multidisciplinary

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3