NeuraLunaDTNet: Feedforward Neural Network-Based Routing Protocol for Delay-Tolerant Lunar Communication Networks

• URL: http://arxiv.org/abs/2403.20199v2
• Date: Sun, 7 Apr 2024 08:40:45 GMT
• Title: NeuraLunaDTNet: Feedforward Neural Network-Based Routing Protocol for Delay-Tolerant Lunar Communication Networks
• Authors: Parth Patel, Milena Radenkovic,
• Abstract summary: Space poses challenges such as severe delays, hard-to-predict routes and communication disruptions. Traditional DTN routing protocols fall short of delivering optimal performance, due to the inherent complexities of space communication. We propose utilising a feedforward neural network to develop a novel protocol NeuraLunaDTNet, which enhances the efficiency of lunar communication.
• Score: 0.46040036610482665
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Space Communication poses challenges such as severe delays, hard-to-predict routes and communication disruptions. The Delay Tolerant Network architecture, having been specifically designed keeping such scenarios in mind, is suitable to address some challenges. The traditional DTN routing protocols fall short of delivering optimal performance, due to the inherent complexities of space communication. Researchers have aimed at using recent advancements in AI to mitigate some routing challenges [9]. We propose utilising a feedforward neural network to develop a novel protocol NeuraLunaDTNet, which enhances the efficiency of the PRoPHET routing protocol for lunar communication, by learning contact plans in dynamically changing spatio-temporal graph.

Related papers

• AI Flow at the Network Edge [58.31090055138711]
  AI Flow is a framework that streamlines the inference process by jointly leveraging the heterogeneous resources available across devices, edge nodes, and cloud servers. This article serves as a position paper for identifying the motivation, challenges, and principles of AI Flow.
  arXiv  Detail & Related papers  (2024-11-19T12:51:17Z)
• Toward Autonomous Cooperation in Heterogeneous Nanosatellite Constellations Using Dynamic Graph Neural Networks [0.0]
  The paper proposes a novel approach to overcome the challenges by modeling the constellations and CP as dynamic networks. The trained neural network can predict the network delay with a mean absolute error of 3.6 minutes. Simulation results show that the proposed method can successfully design a contact plan for large satellite networks, improving the delay by 29.1%, similar to a traditional approach.
  arXiv  Detail & Related papers  (2024-03-01T17:26:02Z)
• Dynamic Routing for Integrated Satellite-Terrestrial Networks: A Constrained Multi-Agent Reinforcement Learning Approach [41.714453335170404]
  We study packet routing with ground stations and satellites working jointly to transmit packets. We propose a novel constrained Multi-Agent reinforcement learning (MARL) dynamic routing algorithm named CMADR. Results demonstrate that CMADR reduces the packet delay by a minimum of 21% and 15%, while meeting stringent energy consumption and packet loss rate constraints, outperforming several baseline algorithms.
  arXiv  Detail & Related papers  (2023-12-23T03:36:35Z)
• Deep Reinforcement Learning Aided Packet-Routing For Aeronautical Ad-Hoc Networks Formed by Passenger Planes [99.54065757867554]
  We invoke deep reinforcement learning for routing in AANETs aiming at minimizing the end-to-end (E2E) delay. A deep Q-network (DQN) is conceived for capturing the relationship between the optimal routing decision and the local geographic information observed by the forwarding node. We further exploit the knowledge concerning the system's dynamics by using a deep value network (DVN) conceived with a feedback mechanism.
  arXiv  Detail & Related papers  (2021-10-28T14:18:56Z)
• Deep Learning Aided Packet Routing in Aeronautical Ad-Hoc Networks Relying on Real Flight Data: From Single-Objective to Near-Pareto Multi-Objective Optimization [79.96177511319713]
  We invoke deep learning (DL) to assist routing in aeronautical ad-hoc networks (AANETs) A deep neural network (DNN) is conceived for mapping the local geographic information observed by the forwarding node into the information required for determining the optimal next hop. We extend the DL-aided routing algorithm to a multi-objective scenario, where we aim for simultaneously minimizing the delay, maximizing the path capacity, and maximizing the path lifetime.
  arXiv  Detail & Related papers  (2021-10-28T14:18:22Z)
• Packet Routing with Graph Attention Multi-agent Reinforcement Learning [4.78921052969006]
  We develop a model-free and data-driven routing strategy by leveraging reinforcement learning (RL) Considering the graph nature of the network topology, we design a multi-agent RL framework in combination with Graph Neural Network (GNN)
  arXiv  Detail & Related papers  (2021-07-28T06:20:34Z)
• Learning Autonomy in Management of Wireless Random Networks [102.02142856863563]
  This paper presents a machine learning strategy that tackles a distributed optimization task in a wireless network with an arbitrary number of randomly interconnected nodes. We develop a flexible deep neural network formalism termed distributed message-passing neural network (DMPNN) with forward and backward computations independent of the network topology.
  arXiv  Detail & Related papers  (2021-06-15T09:03:28Z)
• CARL-DTN: Context Adaptive Reinforcement Learning based Routing Algorithm in Delay Tolerant Network [0.0]
  Delay/Disruption-Tolerant Networks (DTN) invented to describe and cover all types of long-delay, disconnected, intermittently connected networks. This study proposes context-adaptive reinforcement learning based routing protocol to determine optimal replicas of the message based on the real-time density.
  arXiv  Detail & Related papers  (2021-05-02T20:08:17Z)
• Relational Deep Reinforcement Learning for Routing in Wireless Networks [2.997420836766863]
  We develop a distributed routing strategy based on deep reinforcement learning that generalizes to diverse traffic patterns, congestion levels, network connectivity, and link dynamics. Our algorithm outperforms shortest path and backpressure routing with respect to packets delivered and delay per packet.
  arXiv  Detail & Related papers  (2020-12-31T16:28:21Z)
• Progressive Tandem Learning for Pattern Recognition with Deep Spiking Neural Networks [80.15411508088522]
  Spiking neural networks (SNNs) have shown advantages over traditional artificial neural networks (ANNs) for low latency and high computational efficiency. We propose a novel ANN-to-SNN conversion and layer-wise learning framework for rapid and efficient pattern recognition.
  arXiv  Detail & Related papers  (2020-07-02T15:38:44Z)
• Constructing Geographic and Long-term Temporal Graph for Traffic Forecasting [88.5550074808201]
  We propose Geographic and Long term Temporal Graph Convolutional Recurrent Neural Network (GLT-GCRNN) for traffic forecasting. In this work, we propose a novel framework for traffic forecasting that learns the rich interactions between roads sharing similar geographic or longterm temporal patterns.
  arXiv  Detail & Related papers  (2020-04-23T03:50:46Z)

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.