Deep Learning Aided Packet Routing in Aeronautical Ad-Hoc Networks Relying on Real Flight Data: From Single-Objective to Near-Pareto Multi-Objective Optimization

• URL: http://arxiv.org/abs/2110.15145v1
• Date: Thu, 28 Oct 2021 14:18:22 GMT
• Title: Deep Learning Aided Packet Routing in Aeronautical Ad-Hoc Networks Relying on Real Flight Data: From Single-Objective to Near-Pareto Multi-Objective Optimization
• Authors: Dong Liu, Jiankang Zhang, Jingjing Cui, Soon-Xin Ng, Robert G. Maunder, Lajos Hanzo
• Abstract summary: 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.
• Score: 79.96177511319713
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Data packet routing in aeronautical ad-hoc networks (AANETs) is challenging due to their high-dynamic topology. In this paper, we invoke deep learning (DL) to assist routing in AANETs. We set out from the single objective of minimizing the end-to-end (E2E) delay. Specifically, 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. The DNN is trained by exploiting the regular mobility pattern of commercial passenger airplanes from historical flight data. After training, the DNN is stored by each airplane for assisting their routing decisions during flight relying solely on local geographic information. Furthermore, 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. Our simulation results based on real flight data show that the proposed DL-aided routing outperforms existing position-based routing protocols in terms of its E2E delay, path capacity as well as path lifetime, and it is capable of approaching the Pareto front that is obtained using global link information.

Related papers

• ARDDQN: Attention Recurrent Double Deep Q-Network for UAV Coverage Path Planning and Data Harvesting [3.746548465186206]
  Unmanned Aerial Vehicles (UAVs) have gained popularity in data harvesting (DH) and coverage path planning ( CPP) We propose the ARDDQN (Attention-based Recurrent Double Deep Q Network), which integrates double deep Q-networks (DDQN) with recurrent neural networks (RNNs) We employ a structured environment map comprising a compressed global environment map and a local map showing the UAV agent's locate efficiently scaling to large environments.
  arXiv  Detail & Related papers  (2024-05-17T16:53:19Z)
• Multi-Agent Deep Reinforcement Learning for Distributed Satellite Routing [7.793857269225969]
  This paper introduces a Multi-Agent Deep Reinforcement Learning (MA-DRL) approach for routing in Low Earth Orbit Satellite Constellations (LSatCs) Results show that MA-DRL efficiently learns optimal routes offline that are then loaded for an efficient distributed routing online.
  arXiv  Detail & Related papers  (2024-02-27T16:36:53Z)
• 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 Routing for Space-Air-Ground Integrated Networks Relying on Real Satellite, Flight, and Shipping Data [79.96177511319713]
  Current maritime communications mainly rely on satellites having meager transmission resources, hence suffering from poorer performance than modern terrestrial wireless networks. With the growth of transcontinental air traffic, the promising concept of aeronautical ad hoc networking relying on commercial passenger airplanes is potentially capable of enhancing satellite-based maritime communications via air-to-ground and multi-hop air-to-air links. We propose space-air-ground integrated networks (SAGINs) for supporting ubiquitous maritime communications, where the low-earth-orbit satellite constellations, passenger airplanes, terrestrial base stations, ships, respectively, serve as the space-, air-,
  arXiv  Detail & Related papers  (2021-10-28T14:12:10Z)
• Trajectory Design for UAV-Based Internet-of-Things Data Collection: A Deep Reinforcement Learning Approach [93.67588414950656]
  In this paper, we investigate an unmanned aerial vehicle (UAV)-assisted Internet-of-Things (IoT) system in a 3D environment. We present a TD3-based trajectory design for completion time minimization (TD3-TDCTM) algorithm. Our simulation results show the superiority of the proposed TD3-TDCTM algorithm over three conventional non-learning based baseline methods.
  arXiv  Detail & Related papers  (2021-07-23T03:33:29Z)
• UAV Path Planning for Wireless Data Harvesting: A Deep Reinforcement Learning Approach [18.266087952180733]
  We propose a new end-to-end reinforcement learning approach to UAV-enabled data collection from Internet of Things (IoT) devices. An autonomous drone is tasked with gathering data from distributed sensor nodes subject to limited flying time and obstacle avoidance. We show that our proposed network architecture enables the agent to make movement decisions for a variety of scenario parameters.
  arXiv  Detail & Related papers  (2020-07-01T15:14:16Z)
• Road Network Metric Learning for Estimated Time of Arrival [93.0759529610483]
  In this paper, we propose the Road Network Metric Learning framework for Estimated Time of Arrival (ETA) It consists of two components: (1) a main regression task to predict the travel time, and (2) an auxiliary metric learning task to improve the quality of link embedding vectors. We show that our method outperforms the state-of-the-art model and the promotion concentrates on the cold links with few data.
  arXiv  Detail & Related papers  (2020-06-24T04:45:14Z)
• Deep Learning based Pedestrian Inertial Navigation: Methods, Dataset and On-Device Inference [49.88536971774444]
  Inertial measurements units (IMUs) are small, cheap, energy efficient, and widely employed in smart devices and mobile robots. Exploiting inertial data for accurate and reliable pedestrian navigation supports is a key component for emerging Internet-of-Things applications and services. We present and release the Oxford Inertial Odometry dataset (OxIOD), a first-of-its-kind public dataset for deep learning based inertial navigation research.
  arXiv  Detail & Related papers  (2020-01-13T04:41:54Z)

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.