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.
|Number of pages||17|
|Early online date||12 Nov 2022|
|Publication status||Published online - 12 Nov 2022|
Bibliographical noteFunding Information:
This work was supported in part by TUOHAI special project 2020 of the Bohai Rim Energy Research Institute of Northeast Petroleum University under Grant HBHZX202002 and the Project of Excellent and Middle-aged Scientific Research Innovation Team of Northeast Petroleum University under Grant KYCXTD201903.
© 2022 by the authors.
- remote sensing data
- big data acquisition
- task scheduling