Joint Speed and Bandwidth Optimized Strategy of UAV-Assisted Data Collection in Post-Disaster Areas

Fu, Yue; Li, Demin; Tang, Qinghua; Zhou, Shuang

doi:10.1109/medcomnet55087.2022.9810444

Cited by 8 publications

(3 citation statements)

References 11 publications

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“…This study found that the speed of the UAV varies depending on the location of the sensors, the amount of data required to be transmitted by them, and their energy usage. [34] and [35] used a system model similar to the one in [29] to solve the problem caused by the ground terminal (GT) distribution density. [34] worked on maximizing the number of GTs that satisfy the constraint for the minimum amount of transmitted data by optimizing…”

Section: Related Workmentioning

confidence: 99%

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Speed-Control Technique for Achieving Fair Uplink Communications With a UAV-Aided Flying Base Station

Mitsui

Nishiyama

2023

IEEE Access

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The use of aerial base stations on unmanned aerial vehicles (UAVs) is a notable solution for providing disaster victims with communication services because they can be quickly deployed immediately after a disaster. In this study, we investigate how a single circling UAV with an aerial base station can be used for collecting uplink information from users on the ground. In this system, users within the coverage area of an aerial base station share the available system bandwidth. Consequently, since a UAV's flying speed is constant, a fairness issue becomes significant among ground users distributed over the service area when their spatial density differs largely from place to place. To solve this fairness issue, we focus on a speed-control technique for the UAV. We begin by formulating the amount of transmitted data from each user equipment (UE) as a function of the communication time, during which the user is within the coverage of the aerial base station, and which depends on the speed of the UAV. The objective function can be defined as a maximization of the minimum amount of data transmitted by each UE. Then, we develop the proposed speed-control technique by analyzing the quantitative relationship between the UAV's flying speed and the amount of data transmitted by each UE. Finally, we use computer simulations to demonstrate the effectiveness of our technique.INDEX TERMS unmanned aerial vehicle (UAV), speed control, fairness, disaster Many communication systems using UAV swarms to cover large areas have been proposed [23], [24], such as the information collection system in [25], in which UAVs fly in a circular trajectory. Timeliness and fairness are two important

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Section: Related Workmentioning

confidence: 99%

“…[34] and [35] used a system model similar to the one in [29] to solve the problem caused by the ground terminal (GT) distribution density. [34] worked on maximizing the number of GTs that satisfy the constraint for the minimum amount of transmitted data by optimizing…”

Section: Related Workmentioning

confidence: 99%

Speed-Control Technique for Achieving Fair Uplink Communications With a UAV-Aided Flying Base Station

Mitsui

Nishiyama

2023

IEEE Access

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“…Aiming at the post-disaster areas where infrastructure has been destroyed, Ref. [13] proposed an efficient data transmission scheme based on a particle swarm algorithm to ensure communication quality. Ref.…”

Section: Introductionmentioning

confidence: 99%

Joint Trajectory Planning, Time and Power Allocation to Maximize Throughput in UAV Network

Wang

Li³

et al. 2023

Drones

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Due to the advantages of strong mobility, flexible deployment, and low cost, unmanned aerial vehicles (UAVs) are widely used in various industries. As a flying relay, UAVs can establish line-of-sight (LOS) links for different scenarios, effectively improving communication quality. In this paper, considering the limited energy budget of UAVs and the existence of multiple jammers, we introduce a simultaneous wireless information and power transfer (SWIPT) technology and study the problems of joint-trajectory planning, time, and power allocation to increase communication performance. Specifically, the network includes multiple UAVs, source nodes (SNs), destination nodes (DNs), and jammers. We assume that the UAVs need to communicate with DNs, the SNs use the SWIPT technology to transmit wireless energy and information to UAVs, and the jammers can interfere with the channel from UAVs to DNs. In this network, our target was to maximize the throughput of DNs by optimizing the UAV’s trajectory, time, and power allocation under the constraints of jammers and the actual motion of UAVs (including UAV energy budget, maximum speed, and anti-collision constraints). Since the formulated problem was non-convex and difficult to solve directly, we first decomposed the original problem into three subproblems. We then solved the subproblems by applying a successive convex optimization technology and a slack variables method. Finally, an efficient joint optimization algorithm was proposed to obtain a sub-optimal solution by using a block coordinate descent method. Simulation results indicated that the proposed algorithm has better performance than the four baseline schemes.

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RETRACTED ARTICLE: Distributed computing model for channel bandwidth allocation and optimization using machine learning techniques

Shan,

Zhang

2023

Opt Quant Electron

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Joint Speed and Bandwidth Optimized Strategy of UAV-Assisted Data Collection in Post-Disaster Areas

Cited by 8 publications

References 11 publications

Speed-Control Technique for Achieving Fair Uplink Communications With a UAV-Aided Flying Base Station

Speed-Control Technique for Achieving Fair Uplink Communications With a UAV-Aided Flying Base Station

Joint Trajectory Planning, Time and Power Allocation to Maximize Throughput in UAV Network

RETRACTED ARTICLE: Distributed computing model for channel bandwidth allocation and optimization using machine learning techniques

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