UAV Swarm-enabled Collaborative Secure Relay Communications with
Time-domain Colluding Eavesdropper
- URL: http://arxiv.org/abs/2310.01980v1
- Date: Tue, 3 Oct 2023 11:47:01 GMT
- Title: UAV Swarm-enabled Collaborative Secure Relay Communications with
Time-domain Colluding Eavesdropper
- Authors: Chuang Zhang, Geng Sun, Qingqing Wu, Jiahui Li, Shuang Liang, Dusit
Niyato and Victor C.M. Leung
- Abstract summary: 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.
- Score: 115.56455278813756
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Unmanned aerial vehicles (UAVs) as aerial relays are practically appealing
for assisting Internet of Things (IoT) network. In this work, we aim to utilize
the UAV swarm to assist the secure communication between the micro base station
(MBS) equipped with the planar array antenna (PAA) and the IoT terminal devices
by collaborative beamforming (CB), so as to counteract the effects of collusive
eavesdropping attacks in time-domain. Specifically, we formulate a UAV
swarm-enabled secure relay multi-objective optimization problem (US2RMOP) for
simultaneously maximizing the achievable sum rate of associated IoT terminal
devices, minimizing the achievable sum rate of the eavesdropper and minimizing
the energy consumption of UAV swarm, by jointly optimizing the excitation
current weights of both MBS and UAV swarm, the selection of the UAV receiver,
the position of UAVs and user association order of IoT terminal devices.
Furthermore, the formulated US2RMOP is proved to be a non-convex, NP-hard and
large-scale optimization problem. Therefore, we propose an improved
multi-objective grasshopper algorithm (IMOGOA) with some specific designs to
address the problem. Simulation results exhibit the effectiveness of the
proposed UAV swarm-enabled collaborative secure relay strategy and demonstrate
the superiority of IMOGOA.
Related papers
- Movable Antenna-Equipped UAV for Data Collection in Backscatter Sensor Networks: A Deep Reinforcement Learning-based Approach [10.115361454176773]
Unmanned aerial vehicles (UAVs) enable flexible data collection from remote backscatter devices (BDs)
We consider equipping a UAV with a directional movable antenna (MA) with high directivity and flexibility.
We develop a deep reinforcement learning (DRL)-based strategy using the azimuth angle and distance between the UAV and each BD to simplify the agent's observation space.
arXiv Detail & Related papers (2024-11-21T09:34:48Z) - UAV-enabled Collaborative Beamforming via Multi-Agent Deep Reinforcement Learning [79.16150966434299]
We formulate a UAV-enabled collaborative beamforming multi-objective optimization problem (UCBMOP) to maximize the transmission rate of the UVAA and minimize the energy consumption of all UAVs.
We use the heterogeneous-agent trust region policy optimization (HATRPO) as the basic framework, and then propose an improved HATRPO algorithm, namely HATRPO-UCB.
arXiv Detail & Related papers (2024-04-11T03:19:22Z) - Multi-Objective Optimization for UAV Swarm-Assisted IoT with Virtual
Antenna Arrays [55.736718475856726]
Unmanned aerial vehicle (UAV) network is a promising technology for assisting Internet-of-Things (IoT)
Existing UAV-assisted data harvesting and dissemination schemes require UAVs to frequently fly between the IoTs and access points.
We introduce collaborative beamforming into IoTs and UAVs simultaneously to achieve energy and time-efficient data harvesting and dissemination.
arXiv Detail & Related papers (2023-08-03T02:49:50Z) - Energy-Efficient Cellular-Connected UAV Swarm Control Optimization [25.299881367750487]
We propose a two-phase command and control (C&C) transmission scheme in a cellular-connected UAV swarm network.
We formulate the problem as a Constrained Markov Decision Process to find the optimal policy.
Our algorithm could maximize the number of UAVs that successfully receive the common C&C under energy constraints.
arXiv Detail & Related papers (2023-03-18T11:42:04Z) - 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) - Multi-Agent Deep Reinforcement Learning For Optimising Energy Efficiency
of Fixed-Wing UAV Cellular Access Points [3.502112118170715]
We propose a multi-agent deep reinforcement learning approach to optimise the energy efficiency of fixed-wing UAV cellular access points.
In our approach, each UAV is equipped with a Dueling Deep Q-Network (DDQN) agent which can adjust the 3D trajectory of the UAV over a series of timesteps.
arXiv Detail & Related papers (2021-11-03T14:49:17Z) - RIS-assisted UAV Communications for IoT with Wireless Power Transfer
Using Deep Reinforcement Learning [75.677197535939]
We propose a simultaneous wireless power transfer and information transmission scheme for IoT devices with support from unmanned aerial vehicle (UAV) communications.
In a first phase, IoT devices harvest energy from the UAV through wireless power transfer; and then in a second phase, the UAV collects data from the IoT devices through information transmission.
We formulate a Markov decision process and propose two deep reinforcement learning algorithms to solve the optimization problem of maximizing the total network sum-rate.
arXiv Detail & Related papers (2021-08-05T23:55:44Z) - 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)
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.