Dynamic Task Scheduling in Remote Sensing Data Acquisition from Open-Access Data Using CloudSim

With the rapid development of cloud computing and network technologies, large-scale remote sensing data collection tasks are receiving more interest from individuals and small and medium-sized enterprises. Large-scale remote sensing data collection has its challenges, including less available node r...

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Bibliographic Details
Published in:Applied sciences 2022-11, Vol.12 (22), p.11508
Main Authors: Wang, Zhibao, Bai, Lu, Liu, Xiaogang, Chen, Yuanlin, Zhao, Man, Tao, Jinhua
Format: Article
Language:English
Subjects:
Algorithms
Back propagation networks
Bandwidths
big data acquisition
CloudSim
Data acquisition
Data collection
Energy consumption
Genetic algorithms
Heuristic
Internet of Things
Neural networks
Performance evaluation
PSO
Remote sensing
remote sensing data
Research methodology
Satellites
Scheduling
Small business
task scheduling
Unmanned aerial vehicles
User satisfaction
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Summary:With the rapid development of cloud computing and network technologies, large-scale remote sensing data collection tasks are receiving more interest from individuals and small and medium-sized enterprises. Large-scale remote sensing data collection has its challenges, including less available node resources, short collection time, and lower collection efficiency. Moreover, public remote data sources have restrictions on user settings, such as access to IP, frequency, and bandwidth. In order to satisfy users’ demand for accessing public remote sensing data collection nodes and effectively increase the data collection speed, this paper proposes a TSCD-TSA dynamic task scheduling algorithm that combines the BP neural network prediction algorithm with PSO-based task scheduling algorithms. Comparative experiments were carried out using the proposed task scheduling algorithms on an acquisition task using data from Sentinel2. The experimental results show that the MAX-MAX-PSO dynamic task scheduling algorithm has a smaller fitness value and a faster convergence speed.
ISSN:2076-3417
2076-3417
DOI:10.3390/app122211508