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

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 envi...

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Veröffentlicht in:Sensors (Basel, Switzerland) Jg. 24; H. 1; S. 208
Hauptverfasser: Liu, Yang, Li, Yong, Cheng, Wei, Wang, Weiguang, Yang, Junhua
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Switzerland MDPI AG 29.12.2023
MDPI
Schlagworte:
Algorithms
cluster algorithm
Communication
Costs
Crowdsourcing
edge computing
Energy consumption
Environmental quality
Flying-machines
Internet of Things
mobile crowdsensing
Optimization
SAGSIN
Sensors
Simulation methods
task allocation
SAGSIN
task allocation
cluster algorithm
edge computing
mobile crowdsensing
ISSN:1424-8220, 1424-8220
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Zusammenfassung: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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ISSN:1424-8220
1424-8220
DOI:10.3390/s24010208