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Collaborative Data Acquisition for UAV-Aided IoT Based on Time-Balancing Scheduling

delete2024-04-15
delete9
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OA
AI
M
Mingyuan Ren
符修文 (Xiuwen Fu)
P
Pasquale Pace
G
Gianluca Aloi
G
Giancarlo Fortino *
DOI:10.1109/JIOT.2023.3339136delete
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摘要

摘要

En 中文
The emergence of the Internet of Things (IoT) has revolutionized various domains by enabling seamless connectivity and real-time data exchange between connected IoT devices. However, in sparse deployment scenarios where sensor nodes (SNs) are sparsely distributed, ensuring low data delivery latency becomes a significant challenge. Our research aims to address this issue by utilizing unmanned aerial vehicles (UAVs) to support IoT networks. In the existing UAV-aided IoT systems, all UAVs are required to return to the base station (BS) to deliver data, which results in significant data delivery latency. To overcome this limitation, we propose a collaborative data acquisition model that uses air-to-air data relay between UAVs. By leveraging the mobility and agility of UAVs, the proposed system facilitates efficient data relay between SNs and the BS. To further optimize the performance of the system, we present a time-balancing scheduling data acquisition (TSDA) scheme. This scheme combines a centripetal-based relay pairing method for UAVs to achieve seamless data relay and a joint scheduling scheme to minimize the hovering time during data delivery. Through extensive simulations, we demonstrate that the proposed TSDA scheme can achieve lower data delivery latency in sparse deployment scenarios compared to existing data acquisition schemes. In addition, the joint scheduling scheme can significantly reduce the hovering time of UAVs so that the collaborative relaying advantage can be better exploited.
Keyword:
Autonomous aerial vehicles
Relays
Data acquisition
Task analysis
Internet of Things
Collaboration
Data models
delivery latency
joint scheduling
time-balancing
unmanned aerial vehicle (UAV)-aided Internet of Things (IoT)

期刊

IEEE Internet of Things Journal 封面图
IEEE Internet of Things Journal
IF:
8.9
论文数:
1.4W
被引数:
7.8W

机构

U
University of Calabria
学者数:
8.2K
论文数: 8.0K
被引数: 7.8K
S
Shanghai Maritime University
学者数:
4.8K
论文数: 4.2K
被引数: 4.7K
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