SREC: Proactive Self-Remedy of Energy-Constrained UAV-Based Networks via
Deep Reinforcement Learning
- URL: http://arxiv.org/abs/2009.08528v1
- Date: Thu, 17 Sep 2020 20:51:17 GMT
- Title: SREC: Proactive Self-Remedy of Energy-Constrained UAV-Based Networks via
Deep Reinforcement Learning
- Authors: Ran Zhang, Miao Wang, and Lin X. Cai
- Abstract summary: Energy-aware control for multiple unmanned aerial vehicles (UAVs) is one of the major research interests in UAV based networking.
We study proactive self-remedy of energy-constrained UAV networks when one or more UAVs are short of energy and about to quit for charging.
We propose an energy-aware optimal UAV control policy which proactively relocates the UAVs when any UAV is about to quit the network.
- Score: 11.065500588538997
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Energy-aware control for multiple unmanned aerial vehicles (UAVs) is one of
the major research interests in UAV based networking. Yet few existing works
have focused on how the network should react around the timing when the UAV
lineup is changed. In this work, we study proactive self-remedy of
energy-constrained UAV networks when one or more UAVs are short of energy and
about to quit for charging. We target at an energy-aware optimal UAV control
policy which proactively relocates the UAVs when any UAV is about to quit the
network, rather than passively dispatches the remaining UAVs after the quit.
Specifically, a deep reinforcement learning (DRL)-based self remedy approach,
named SREC-DRL, is proposed to maximize the accumulated user satisfaction
scores for a certain period within which at least one UAV will quit the
network. To handle the continuous state and action space in the problem, the
state-of-the-art algorithm of the actor-critic DRL, i.e., deep deterministic
policy gradient (DDPG), is applied with better convergence stability. Numerical
results demonstrate that compared with the passive reaction method, the
proposed SREC-DRL approach shows a $12.12\%$ gain in accumulative user
satisfaction score during the remedy period.
Related papers
- Collaborative Reinforcement Learning Based Unmanned Aerial Vehicle (UAV)
Trajectory Design for 3D UAV Tracking [21.520344500526516]
The problem of using one active unmanned aerial vehicle (UAV) and four passive UAVs to localize a 3D target UAV in real time is investigated.
A Z function decomposition based reinforcement learning (ZD-RL) method is proposed to solve this problem.
arXiv Detail & Related papers (2024-01-22T16:21:19Z) - UAV Swarm-enabled Collaborative Secure Relay Communications with
Time-domain Colluding Eavesdropper [115.56455278813756]
Unmanned aerial vehicles (UAV) as aerial relays are practically appealing for assisting Internet Things (IoT) network.
In this work, we aim to utilize the UAV to assist secure communication between the UAV base station and terminal terminal devices.
arXiv Detail & Related papers (2023-10-03T11:47:01Z) - Evidential Detection and Tracking Collaboration: New Problem, Benchmark
and Algorithm for Robust Anti-UAV System [56.51247807483176]
Unmanned Aerial Vehicles (UAVs) have been widely used in many areas, including transportation, surveillance, and military.
Previous works have simplified such an anti-UAV task as a tracking problem, where prior information of UAVs is always provided.
In this paper, we first formulate a new and practical anti-UAV problem featuring the UAVs perception in complex scenes without prior UAVs information.
arXiv Detail & Related papers (2023-06-27T19:30:23Z) - 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) - Responsive Regulation of Dynamic UAV Communication Networks Based on
Deep Reinforcement Learning [16.78151396672782]
We develop an optimal UAV control policy which is capable of identifying the upcoming change in the UAV lineup and relocating the UAVs ahead of the change.
Specifically, a deep reinforcement learning (DRL)-based UAV control framework is developed to maximize the accumulated user satisfaction (US) score for a given time horizon.
In addition, to handle the continuous state and action space, deep deterministic policy gradient (DDPG) algorithm, which is an actor-critic based DRL, is exploited.
arXiv Detail & Related papers (2021-08-25T02:04:13Z) - 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) - UAV-ReID: A Benchmark on Unmanned Aerial Vehicle Re-identification [21.48667873335246]
Recent development in deep learning allows vision-based counter-UAV systems to detect and track UAVs with a single camera.
The coverage of a single camera is limited, necessitating the need for multicamera configurations to match UAVs across cameras.
We propose the first new UAV re-identification data set, UAV-reID, that facilitates the development of machine learning solutions in this emerging area.
arXiv Detail & Related papers (2021-04-13T14:13:09Z) - Efficient UAV Trajectory-Planning using Economic Reinforcement Learning [65.91405908268662]
We introduce REPlanner, a novel reinforcement learning algorithm inspired by economic transactions to distribute tasks between UAVs.
We formulate the path planning problem as a multi-agent economic game, where agents can cooperate and compete for resources.
As the system computes task distributions via UAV cooperation, it is highly resilient to any change in the swarm size.
arXiv Detail & Related papers (2021-03-03T20:54:19Z) - 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)
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.