Energy-Efficient 3D Data Collection for Multi-UAV Assisted Mobile Crowdsensing

Fu, Luwei; Zhao, Zhiwei; Min, Geyong; Miao, Wang; Zhao, Liang; Huang, Wenjie

doi:10.1109/tc.2022.3227869

Cited by 13 publications

(1 citation statement)

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“…They converted the task allocation problem into a dynamic matching problem and presented an MWTA algorithm in order to realize the optimal matching in a time-varying environment. In [ 36 ], L Fu investigated the problem of the failure of data collection in hard-to-reach and infrastructure-restrained urban areas and designed three-dimensional multi-UAV-assisted CrowdSensing, which was called 3DM. Compared with existing methods, 3DM fully utilized the 3D flexibility to enhance device matching and data transfer between UAVs and MDs, which could require less time and energy to accomplish the tasks.…”

Section: Related Workmentioning

confidence: 99%

UAV-Assisted Cluster-Based Task Allocation for Mobile Crowdsensing in a Space–Air–Ground–Sea Integrated Network

Liu,

Li,

Cheng

et al. 2023

Sensors

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Mobile crowdsensing (MCS), which is a grassroots sensing paradigm that utilizes the idea of crowdsourcing, has attracted the attention of academics. More and more researchers have devoted themselves to adopting MCS in space–air–ground–sea integrated networks (SAGSINs). Given the dynamics of the environmental conditions in SAGSINs and the uncertainty of the sensing capabilities of mobile people, the quality and coverage of the sensed data change periodically. To address this issue, we propose a novel UAV-assisted cluster-based task allocation (UCTA) algorithm for MCS in SAGSINs in a two-stage process. We first introduce the edge nodes and establish a three-layer hierarchical system with UAV-assistance, called “Platform–Edge Cluster–Participants”. Moreover, an edge-aided attribute-based cluster algorithm is designed, aiming at organizing tasks into clusters, which significantly diminishes both the communication overhead and computational complexity while enhancing the efficiency of task allocation. Subsequently, a greedy selection algorithm is proposed to select the final combination that performs the sensing task in each cluster. Extensive simulations are conducted comparing the developed algorithm with the other three benchmark algorithms, and the experimental results unequivocally endorse the superiority of our proposed UCTA algorithm.

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Section: Related Workmentioning

confidence: 99%