Distributed Machine Learning for UAV Swarms: Computing, Sensing, and Semantics

• URL: http://arxiv.org/abs/2301.00912v1
• Date: Tue, 3 Jan 2023 01:05:18 GMT
• Title: Distributed Machine Learning for UAV Swarms: Computing, Sensing, and Semantics
• Authors: Yahao Ding, Zhaohui Yang, Quoc-Viet Pham, Zhaoyang Zhang, Mohammad Shikh-Bahaei
• Abstract summary: Distributed learning (DL) enables UAV swarms to intelligently provide communication services, multi-directional remote surveillance, and target tracking. We first introduce several popular DL algorithms such as federated learning (FL), multi-agent Reinforcement Learning (MARL), distributed inference, and split learning. Then, we present several state-of-the-art applications of UAV swarms in wireless communication systems, such us reconfigurable intelligent surface (RIS), virtual reality (VR), semantic communications, and discuss the problems and challenges that DL-enabled UAV swarms can solve in these applications.
• Score: 31.921859542234998
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Unmanned aerial vehicle (UAV) swarms are considered as a promising technique for next-generation communication networks due to their flexibility, mobility, low cost, and the ability to collaboratively and autonomously provide services. Distributed learning (DL) enables UAV swarms to intelligently provide communication services, multi-directional remote surveillance, and target tracking. In this survey, we first introduce several popular DL algorithms such as federated learning (FL), multi-agent Reinforcement Learning (MARL), distributed inference, and split learning, and present a comprehensive overview of their applications for UAV swarms, such as trajectory design, power control, wireless resource allocation, user assignment, perception, and satellite communications. Then, we present several state-of-the-art applications of UAV swarms in wireless communication systems, such us reconfigurable intelligent surface (RIS), virtual reality (VR), semantic communications, and discuss the problems and challenges that DL-enabled UAV swarms can solve in these applications. Finally, we describe open problems of using DL in UAV swarms and future research directions of DL enabled UAV swarms. In summary, this survey provides a comprehensive survey of various DL applications for UAV swarms in extensive scenarios.

Related papers

• Integrating Large Language Models for UAV Control in Simulated Environments: A Modular Interaction Approach [0.3495246564946556]
  This study explores the application of Large Language Models in UAV control. By enabling UAVs to interpret and respond to natural language commands, LLMs simplify the UAV control and usage. The paper discusses several key areas where LLMs can impact UAV technology, including autonomous decision-making, dynamic mission planning, enhanced situational awareness, and improved safety protocols.
  arXiv  Detail & Related papers  (2024-10-23T06:56:53Z)
• Graph Koopman Autoencoder for Predictive Covert Communication Against UAV Surveillance [29.15836826461713]
  Low Probability of Detection (LPD) communication aims to obscure the very presence of radio frequency (RF) signals. Unmanned Aerial Vehicles (UAVs) can detect RF signals from the ground by hovering over specific areas of interest. We introduce a novel framework that combines graph neural networks (GNN) with Koopman theory to predict the trajectories of multiple fixed-wing UAVs.
  arXiv  Detail & Related papers  (2024-01-23T23:42:55Z)
• Machine Learning-Aided Operations and Communications of Unmanned Aerial Vehicles: A Contemporary Survey [43.573379573511765]
  The ongoing amalgamation of UAV and ML techniques is creating a significant synergy and empowering UAVs with unprecedented intelligence and autonomy. This survey aims to provide a timely and comprehensive overview of ML techniques used in UAV operations and communications.
  arXiv  Detail & Related papers  (2022-11-07T15:34:36Z)
• Federated Deep Learning Meets Autonomous Vehicle Perception: Design and Verification [168.67190934250868]
  Federated learning empowered connected autonomous vehicle (FLCAV) has been proposed. FLCAV preserves privacy while reducing communication and annotation costs. It is challenging to determine the network resources and road sensor poses for multi-stage training.
  arXiv  Detail & Related papers  (2022-06-03T23:55:45Z)
• Optimization for Master-UAV-powered Auxiliary-Aerial-IRS-assisted IoT Networks: An Option-based Multi-agent Hierarchical Deep Reinforcement Learning Approach [56.84948632954274]
  This paper investigates a master unmanned aerial vehicle (MUAV)-powered Internet of Things (IoT) network. We propose using a rechargeable auxiliary UAV (AUAV) equipped with an intelligent reflecting surface (IRS) to enhance the communication signals from the MUAV. Under the proposed model, we investigate the optimal collaboration strategy of these energy-limited UAVs to maximize the accumulated throughput of the IoT network.
  arXiv  Detail & Related papers  (2021-12-20T15:45:28Z)
• 3D UAV Trajectory and Data Collection Optimisation via Deep Reinforcement Learning [75.78929539923749]
  Unmanned aerial vehicles (UAVs) are now beginning to be deployed for enhancing the network performance and coverage in wireless communication. It is challenging to obtain an optimal resource allocation scheme for the UAV-assisted Internet of Things (IoT) In this paper, we design a new UAV-assisted IoT systems relying on the shortest flight path of the UAVs while maximising the amount of data collected from IoT devices.
  arXiv  Detail & Related papers  (2021-06-06T14:08:41Z)
• Serverless Federated Learning for UAV Networks: Architecture, Challenges, and Opportunities [27.366598254560994]
  Unmanned aerial vehicles (UAVs) are envisioned to support extensive applications in next-generation wireless networks in both civil and military fields. We propose a novel architecture called SELF-UN, which enables FL within UAV networks without a central entity.
  arXiv  Detail & Related papers  (2021-04-15T16:11:13Z)
• Anti-UAV: A Large Multi-Modal Benchmark for UAV Tracking [59.06167734555191]
  Unmanned Aerial Vehicle (UAV) offers lots of applications in both commerce and recreation. We consider the task of tracking UAVs, providing rich information such as location and trajectory. We propose a dataset, Anti-UAV, with more than 300 video pairs containing over 580k manually annotated bounding boxes.
  arXiv  Detail & Related papers  (2021-01-21T07:00:15Z)
• Artificial Intelligence for UAV-enabled Wireless Networks: A Survey [72.10851256475742]
  Unmanned aerial vehicles (UAVs) are considered as one of the promising technologies for the next-generation wireless communication networks. Artificial intelligence (AI) is growing rapidly nowadays and has been very successful. We provide a comprehensive overview of some potential applications of AI in UAV-based networks.
  arXiv  Detail & Related papers  (2020-09-24T07:11:31Z)
• Artificial Intelligence Aided Next-Generation Networks Relying on UAVs [140.42435857856455]
  Artificial intelligence (AI) assisted unmanned aerial vehicle (UAV) aided next-generation networking is proposed for dynamic environments. In the AI-enabled UAV-aided wireless networks (UAWN), multiple UAVs are employed as aerial base stations, which are capable of rapidly adapting to the dynamic environment. As a benefit of the AI framework, several challenges of conventional UAWN may be circumvented, leading to enhanced network performance, improved reliability and agile adaptivity.
  arXiv  Detail & Related papers  (2020-01-28T15:10:22Z)

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.