## 2 Methods  
Given the massive amount of public remote sensing data collected using limited resource nodes, this paper is mainly focused on studying the large-scale public remote sensing matching problem between data collection tasks and collection nodes. First, a model of multi-objective task scheduling is proposed with the consideration of time limit constraints, energy consumption constraints, cost constraints, and the task scheduling objective function. Then, an improved multi-objective PSO optimization algorithm is proposed to further improve the allocation efficiency. Finally, multiple groups of simulationsfor comparative experiments were designed to compare the task execution time, energy consumption, and cost of various allocation schemes under various task scheduling.  
The task scheduling architecture for conducting public remote sensing data collection is shown in Figure 1. A web crawler is used to obtain remote sensing data task Uniform Resource Locators (URLs) in batches from public remote sensing data sources and add them to the task pool. The scheduler selects a task set of an appropriate size from the task pool to match the execution resource set and outputs the corresponding scheduling scheme. Each executor selects related tasks for execution in strict accordance with the respective task sequence in the scheduler's output task scheduling plan, and the prediction module predicts the future execution speed based on the historical execution records of each executor. The scheduler dynamically adjusts the current task scheduling scheme according to the prediction results of the prediction module and the task execution of each actuator (it only reassigns the tasks that have not been executed in the task set; the tasks that have been executed in the task set are not within the scope of rescheduling). For tasks completed by each actuator, for example, the quality of remote sensing data collected by each actuator needs to be verified. Qualified data will then be entered into the storage center, while unqualified tasks need to be re-collected.  
According to the scheduling architecture of the open remote sensing data acquisition task in Figure 1, the following assumptions are made during the execution of the task:  
* The actuators perform tasks independently and do not interfere with each other.
* The choice of the task set size in the task pool is defined by the user, and the number of task sets is usually much larger than the tasks being executed. Users can select an appropriate number of task sets according to the size of each task in the task set and the execution speed of each actuator so as to avoid some time-limited tasks being in a waiting state for a long time.
* If the task URL has not been validated, each task URL will only be executed by an executor and only once; that is, there is no execution conflict in the task.
* In order to speed up the training, the prediction module adopts the strategy of incremental learning of the execution history.  
Figure 1: Task scheduling architecture diagram.  
* The update frequency of the scheduling algorithm in the scheduler is similar to that of the prediction module. After the prediction module produces new prediction results, task rescheduling is started immediately.