利用低地球轨道卫星网络的容迟数据收集架构分析
Analysis of a Delay-Tolerant Data Harvest Architecture Leveraging Low Earth Orbit Satellite Networks
AI总结：
本文提出基于Cox点过程的LEO卫星容迟数据收集网络模型，推导服务时间比例、过境上传量、容量与延迟分布等指标。
摘要：
低地球轨道（LEO）卫星能够到达地球所有区域，可在没有地面基础设施的情况下，从位于偏远地区的地面用户收集容迟数据。本工作旨在评估一种数据收集网络架构，其中用户生成数据，LEO卫星在经过时从用户处收集数据。通过开发一种新颖的随机几何Cox点过程模型，该模型同时生成轨道以及LEO卫星收集器在这些轨道上的运动，我们分析了此类网络的关键性能指标，并推导了以下内容：（i）典型用户由LEO卫星收集器服务的平均时间比例，（ii）每次卫星过境上传的平均数据量，（iii）所提出网络模型的最大收集容量，以及（iv）所提出网络中的延迟分布。这些关键指标被表示为关键网络变量的函数，例如平均轨道数$\lambda$和每条轨道上的平均卫星数$\mu$。本工作提供了丰富全面的分析结果以及对这些结果的实际解释，评估了LEO卫星容迟使用的潜力，同时也作为一个通用框架，用于分析、设计和优化容迟LEO卫星网络。
英文摘要：
Reaching all regions of Earth, low Earth orbit (LEO) satellites can harvest delay-tolerant data from remotely located users on Earth without ground infrastructure. This work aims to assess a data harvest network architecture where users generate data and LEO satellites harvest data from users when passing by. By developing a novel stochastic geometry Cox point process model that simultaneously generates orbits and the motion of LEO satellite harvesters on them, we analyze key performance indices of such a network by deriving the following: (i) the average fraction of time that the typical user is served by LEO satellite harvesters, (ii) the average amount of data uploaded per each satellite pass, (iii) the maximum harvesting capacity of the proposed network model, and (iv) the delay distribution in the proposed network. These key metrics are given as functions of key network variables such as $λ$ the mean number of orbits and $μ$ the mean number of satellites per orbit. Providing rich comprehensive analytical results and practical interpretations of these results, this work assesses the potential of the delay-tolerant use of LEO satellites and also serves as a versatile framework to analyze, design, and optimize delay-tolerant LEO satellite networks.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
