发表机构
National Tsing Hua University（国立清华大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究针对延迟容忍网络的性能下降问题，提出基于PPO框架的JUROR算法，联合优化无人机飞行与机会路由，仿真显示其在四种流量模式下优于PRoPHET和MaxProp算法。
AI 中文摘要
延迟容忍网络（DTN）的日益部署，使得存储-携带-转发（SCF）通信在连接稀疏的场景下不可或缺。然而，间歇性接触、有限缓存以及消息的有限生存时间（TTL）往往会导致消息投递稀疏和拥塞，进而造成端到端性能的大幅下降。为应对这一挑战，本研究探索去中心化机会路由与可控无人机（UAV）飞行的联合优化，旨在通过离散的无人机航向扩大未来接触机会，同时在接触受限的观测条件下实现逐节点的消息复制。基于该架构，我们研究了集中式训练与去中心化执行（CTDE）下的协作因子化路由——无人机控制问题，并提出JUROR算法（基于近端策略优化（PPO）框架的无人机飞行与机会路由联合算法）。在设计中，我们首先将该问题建模为具有序列运动-路由耦合及每步团队奖励的因子化部分可观测马尔可夫决策过程；随后，去中心化执行器基于局部观测值采取行动，而训练阶段的评论家则利用全局统计信息，可选的多时间步热点预测器可提供辅助监督。针对四种流量模式的仿真结果表明，在保持接触受限的去中心化执行特性的同时，该算法相比PRoPHET和MaxProp算法实现了有效性能提升。
英文摘要
The growing deployment of delay-tolerant networks (DTNs) has made store-carry-forward (SCF) communication indispensable under sparse connectivity. However, intermittent contacts, finite buffers, and limited message time-to-live (TTL) often give rise to sparse delivery and congestion, leading to substantial end-to-end performance degradation. To address this challenge, this study explores the joint optimization of decentralized opportunistic routing and controllable unmanned aerial vehicle (UAV) flight, aiming to enlarge future contacts through discrete UAV headings while enabling per-node replication under contact-limited observations. Building upon this architecture, we study cooperative factored routing--UAV control under centralized training and decentralized execution (CTDE) and propose JUROR (Joint UAV flight and Opportunistic Routing, based on the proximal policy optimization (PPO) framework. In our design, we first cast the problem as a factored partially observable Markov decision process with sequential motion--routing coupling and a per-step team reward; subsequently, decentralized actors act on local observations while a training-time critic uses global statistics, and an optional multi-horizon hotspot predictor provides auxiliary supervision. Simulation results over four traffic modes demonstrate effective gains over PRoPHET and MaxProp, while retaining contact-limited decentralized execution.
