# Distributed Coordination of Space-Ground Multiresources for Remote Sensing Missions
## 4 Distributed Coordinated Resource Scheduling Algorithm Design Based on ADMM
### Algorithm Implementation
In the first stage, each RSS system allocates observation resources and local communication resources to their missions and then sends relay request to the DRS system for the missions lacking communication resources. In the second stage, the DRS system assigns the relay resources. 3. Centralized Coordinated Resource Scheduling (CCRS): There exists a central server to schedule the missions of all the RSS systems with global network information in a centralized manner. Note that CCRS is employed as a baseline algorithm because it is a centralized coordinated method with the ideal condition. The proposed DCRS algorithm and the above three scheduling schemes are implemented through MATLAB programming. By testing them in our simulation scenario, we compare the proposed DCRS algorithm with other three scheduling schemes in terms of the following three metrics [10; 13]:

1. Number of successfully scheduled missions: The number of missions which have been successfully scheduled after employing the proposed mission schedule algorithm or the comparing algorithm. 2. Total working time of the ground stations: The sum time that all the ground stations used to receive data from RSS



More specifically, the NRRS has the largest ground station working time, while the NCRS has the least, as shown in Figure 4. Moreover, the total working time of ground stations under the CCRS and DCRS is very close. This is because under the NRRS scheme, only ground stations can be used to download acquired data. NCRS always overestimate the relay resource that can be used due to lack of coordination among RSS systems and the DRS system. As we can observe from Figure 5, the total DRS working time of proposed DCRS algorithm is 20% larger than NCRS scheme, and it is close to that of CCRS. This is because in the second stage of NCRS, collision of the relay resources may be raised among the RSS systems due to the lack of coordination. Therefore, compared with NCRS, the proposed DCRS gains the resource utilization improvements of both ground stations and DRSs from the advantage of coordination. Figure 6 depicts the number of successfully scheduled missions with increasing length of feasible scheduling window. It can be observed that the performance of the proposed DCRS scheme is very close to the CCRS scheme for the missions with either short or long feasible scheduling windows. The gain of the proposed DCRS scheme compared to the NCRS scheme increases with feasible scheduling window length. Moreover, compared with the nearly linear increase in the NRRS scheme, the growth rate of DCRS slows down when the length of the feasible scheduling window becomes large. Figure 4: Total working time of the ground stations versus the number of mission requests per RSS system. Figure 3: Number of successfully scheduled missions versus the number of mission requests per RSS system. To further dig up the philosophy behind the performance of different lengths of feasible scheduling windows, we investigate the total working time of the ground stations and DRSs with varying lengths of feasible scheduling windows, which are depicted in Figures 7 and 8. As we can see, the working time of ground stations increases approximately linearly with the length of feasible scheduling windows, while the growth working time of DRSs is small, especially when the feasible scheduling window is long. This is because for the missions downloaded through ground stations, both observation and transmission may need to wait a long time for the RSS to fly above the observation object/ground station. In comparison, for the missions downloaded through DRSs, only the observation process may require a long time, because an RSS is visible to at least one DRS most of the time. Moreover, we can observe that the improvement of the proposed algorithm compared to NCRS increases with the feasible scheduling window length in both Figures 7 and 8. This is because the longer the feasible scheduling window, the larger the degree of freedom for resource allocation.