Joint UAV Flight and Opportunistic Routing under Reinforcement Learning for Delay-Tolerant Networks

• URL: http://arxiv.org/abs/2608.04590v1
• Date: Wed, 05 Aug 2026 08:51:41 GMT
• Title: Joint UAV Flight and Opportunistic Routing under Reinforcement Learning for Delay-Tolerant Networks
• Abstract summary: Delay-tolerant networks (DTNs) have made store-carry-forward (SCF) communication indispensable under sparse connectivity.<n> intermittent contacts, finite buffers, and limited message time-to-live (TTL) often give rise to sparse delivery and congestion.<n>This study explores the joint optimization of decentralized opportunistic routing and controllable unmanned aerial vehicle (UAV) flight.
• Score: 4.82972978331848
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

Related papers

• MA-HEAD-Net: Adaptive Rule-Guided Multi-Agent DRL for AoI Minimization in UAV-Assisted Emergency Networks [71.01350912967922]
  In post-disaster scenarios, unmanned aerial vehicles (UAVs) are critical for establishing emergency communication networks.<n>This paper investigates age of information (AoI) minimization for UAV-assisted emergency communications with heterogeneous emergency services.<n>We develop MA-HEAD-Net, an adaptive rule-guided multi-agent deep reinforcement learning framework.
  arXiv  Detail & Related papers  (2026-08-02T10:05:50Z)
• DSIP: A Dynamic Coordination Planner for Signal-Free Intersections using Diffusion-Model-Based Multi-Agent Motion Planning [13.207796958340067]
  Traffic signal control at urban intersections introduces stop-and-go behavior, resulting in increased delays and reduced traffic efficiency.<n> trajectory-level coordination has emerged as a high-potential strategy to augment or transcend conventional phase-based management.<n>This paper proposes DSIP (Diffusion-model-based Signal-free Intersection Planner), a multi-agent motion planning framework driven by a generative diffusion process.
  arXiv  Detail & Related papers  (2026-06-29T01:35:13Z)
• Spatio-Temporal Attention Enhanced Multi-Agent DRL for UAV-Assisted Wireless Networks with Limited Communications [73.26813382994867]
  We propose a delay-tolerant multi-agent deep reinforcement learning algorithm that integrates a delay-penalized reward to encourage information sharing among UAVs.<n>We show that our new approach achieves over 50% reduction in information delay and 75% throughput gain compared to the conventional MADRL.<n>It is also effective in reducing the need for UAVs' information exchange and thus fostering practical deployment of MADRL in UAV-assisted wireless networks.
  arXiv  Detail & Related papers  (2026-03-23T05:42:50Z)
• Blockchain-Enabled Routing for Zero-Trust Low-Altitude Intelligent Networks [77.17664010626726]
  We focus on the routing with multiple UAV clusters in low-altitude intelligent networks (LAINs)<n>To minimize the damage caused by potential threats, we present the zero-trust architecture with the software-defined perimeter and blockchain techniques.<n>We show that the proposed framework reduces the average E2E delay by 59% and improves the TSR by 29% on average compared to benchmarks.
  arXiv  Detail & Related papers  (2026-02-27T04:30:35Z)
• Agentic Spatio-Temporal Grounding via Collaborative Reasoning [80.83158605034465]
  Temporal Video Grounding aims to retrieve thetemporal tube of a target object or person in a video given a text query.<n>We propose the Agentic Spatio-Temporal Grounder (ASTG) framework for the task of STVG towards an open-world and training-free scenario.<n>Specifically, two specialized agents SRA (Spatial Reasoning Agent) and TRA (Temporal Reasoning Agent) constructed leveraging on modern Multimoal Large Language Models (MLLMs)<n>Experiments on popular benchmarks demonstrate the superiority of the proposed approach where it outperforms existing weakly-supervised and zero-shot approaches by a margin
  arXiv  Detail & Related papers  (2026-02-10T10:16:27Z)
• Multi-Agent Deep Reinforcement Learning for Collaborative UAV Relay Networks under Jamming Atatcks [36.380478794869234]
  This paper formulates this challenge as a cooperative Multi-Agent Reinforcement Learning (MARL) problem, solved using the Training with Decentralized Execution (CTDE) framework.<n>Our proposed framework significantly outperforms baselines, increasing total system throughput by approximately 50% while simultaneously achieving a near-zero collision rate.<n>A key finding is that the agents develop an emergent anti-jamming strategy without explicit programming.
  arXiv  Detail & Related papers  (2025-12-09T08:11:21Z)
• Trajectory Design for UAV-Based Low-Altitude Wireless Networks in Unknown Environments: A Digital Twin-Assisted TD3 Approach [62.11847362756054]
  Unmanned aerial vehicles (UAVs) are emerging as key enablers for low-altitude wireless network (LAWN)<n>We propose a digital twin (DT)-assisted training and deployment framework.<n>In this framework, the UAV transmits integrated sensing and communication signals to provide communication services to ground users, while simultaneously collecting echoes that are uploaded to the DT server to progressively construct virtual environments (VEs)<n>These VEs accelerate model training and are continuously updated with real-time UAV sensing data during deployment, supporting decision-making and enhancing flight safety.
  arXiv  Detail & Related papers  (2025-10-28T10:05:53Z)
• Multi-Agent Reinforcement Learning for Offloading Cellular Communications with Cooperating UAVs [21.195346908715972]
  Unmanned aerial vehicles present an alternative means to offload data traffic from terrestrial BSs. This paper presents a novel approach to efficiently serve multiple UAVs for data offloading from terrestrial BSs.
  arXiv  Detail & Related papers  (2024-02-05T12:36:08Z)
• Learning-Based UAV Trajectory Optimization with Collision Avoidance and Connectivity Constraints [0.0]
  Unmanned aerial vehicles (UAVs) are expected to be an integral part of wireless networks. In this paper, we reformulate the multi-UAV trajectory optimization problem with collision avoidance and wireless connectivity constraints. We propose a decentralized deep reinforcement learning approach to solve the problem.
  arXiv  Detail & Related papers  (2021-04-03T22:22:20Z)
• Federated Learning in the Sky: Joint Power Allocation and Scheduling with UAV Swarms [98.78553146823829]
  Unmanned aerial vehicle (UAV) swarms must exploit machine learning (ML) in order to execute various tasks. In this paper, a novel framework is proposed to implement distributed learning (FL) algorithms within a UAV swarm.
  arXiv  Detail & Related papers  (2020-02-19T14:04:01Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.