Mega-constellation networks (MCNs) face the significant threats of regional service denial attacks. To improve the robustness of regional services in MCNs against such attacks, a cost-effective approach is to identify key node sets for targeted protection efforts. This paper formally defines the key node set analysis problem for regional service denial in MCNs and develops a comprehensive solution framework. First, we develop a regional service capability analysis model that considers the dynamic collaboration of multiple satellites within regional communication service scenarios in MCNs, alongside a temporal network model for their collaborative relationships. Next, we design a multi-satellite criticality metric that quantifies the multi-dimensional impacts of satellite node set failures on regional service capabilities. Building on these, we construct a mixed-integer programming-based key node set analysis model to achieve precise identification of key node sets. Finally, simulation experiments are conducted to verify and analyze the proposed methods, providing insights to enhance the robustness of regional services in MCNs.

The number and variety of observation data have increased significantly with the intellectualization of Earth Observation Satellites (EOSs). In order to make efficient use of these observation data, this paper proposes a novel framework for predicting observation demand using a CBiLAM Network, which is the first of its kind to analyze the problem of EOSs demand from a spatio-temporal perspective. In contrast to traditional deep learning methods, which rely on historical time series data at specific locations, our approach constructs spatio-temporal data sets to better capture the complex dependencies between different locations and times. Simulation experiments on observation demand prediction and observation area prediction show that our proposed method achieves satisfactory performance, illustrating its potential for improving future EOS observation scheduling.

Fault-tolerance plays an important role in improving the reliability of multiple earth-observing satellites, especially in emergent scenarios such as obtaining photographs on battlefields or earthquake areas. Fault tolerance can be implemented through scheduling approaches. Unfortunately, little attention has been paid to fault-tolerant scheduling on satellites. To address this issue, we propose a novel dynamic fault-tolerant scheduling model for real-time tasks running on multiple observation satellites. In this model, the primary-backup policy is employed to tolerate one satellite's permanent failure at one time instant. In the light of the fault-tolerant model, we develop a novel fault-tolerant satellite scheduling algorithm named FTSS. To improve the resource utilization, we apply the overlapping technology that includes primary-backup copy overlapping (i.e., PB overlapping) and backup-backup copy overlapping (i.e., BB overlapping). According to the satellites characterized with time windows for observations, we extensively analyze the overlapping mechanism on satellites. We integrate the overlapping mechanism with FTSS, which employs the task merging strategies including primary-backup copy merging (i.e., PB merging), backup-backup copy merging (i.e., BB merging) and primary-primary copy merging (i.e., PP merging). These merging strategies are used to decrease the number of tasks required to be executed, thereby enhancing system schedulability. To demonstrate the superiority of our FTSS, we conduct extensive experiments using the real-world satellite parameters supplied from the satellite tool kit or STK; we compare FTSS with the three baseline algorithms, namely, NMFTSS, NOFTSS, and NMNOFTSS. The experimental results indicate that FTSS efficiently improves the scheduling quality of others and is suitable for fault-tolerant satellite scheduling.

Fault-tolerant scheduling is an efficient approach to improving the reliability of multiple earth-observing satellites especially in some emergent scenarios such as obtaining photographs on battlefields or earthquake areas. Unfortunately, little work has been done to deal with the fault-tolerant scheduling on satellites. To address this issue, this paper presents a novel dynamic fault-tolerant scheduling model using primary-backup policy to tolerate one satellite's permanent failure at one time instant. On this basis, we propose a novel fault-tolerant satellite scheduling algorithm named FTSS, in which an overlapping technology is adopted to improve the resource utilization. Besides, the FTSS employs the task merging strategies to further enhance the schedulability. To demonstrate the superiority of our FTSS, we conduct extensive experiments by simulations using real-world satellite parameters from STK to compare FTSS with other baseline algorithms. The experimental results indicate that FTSS efficiently improves the scheduling quality of others and is suitable for fault-tolerant satellite scheduling.

Scheduling plays a significant role in improving observation effectiveness of distributed imaging satellites. Although extensive satellite scheduling algorithms have been proposed, none of them focuses on dynamic scheduling for emergency tasks. In this paper, a novel multi-objective dynamic scheduling model for emergency tasks on distributed imaging satellites is established for the first time. To improve user's satisfaction ratio and resource utilization, we propose the task merging strategy: establishing a task merging graph (TMG) model and proposing a task merging algorithm-CP-TM based on clique partition. In addition, a rehabilitation technique is suggested to overcome the disadvantage that task merging will make tasks have less imaging opportunities. To further enhance the schedulability, the task backward shift in the waiting sequence is considered in our study. Furthermore, a novel dynamic scheduling algorithm called TMBSR-DES is presented, which comprehensively considers task merging, backward shift, and rehabilitation. To demonstrate the superiority of our TMBSR-DES, we conduct extensive experiments by simulations to compare TMBSR-DES with three existing algorithm-RBHA, RTSSA, and LSA, as well as three baseline algorithms-BS-DES, TMR-DES, and TMBS-DES. The experimental results indicate that TMBSR-DES outperforms the others and is suitable for emergency task scheduling.

Reducing energy consumption has become a major goal in designing modern heterogeneous computing systems. Conventional energy-efficient scheduling strategies developed on this kind of systems mainly focus on energy saving, lacking of the consideration of users' expectations such as expected finish time. Again, the system elasticity is not sufficiently taken into account while making scheduling decisions. In this paper, we develop a novel scheduling strategy named energy-efficient elastic (3E) scheduling for aperiodic, and independent tasks on DVS-enabled heterogeneous computing systems. The 3E strategy concentrates on adjusting tasks' supply voltages according to the system workload, thereby making the best trade-offs between energy conservation and users' expected finish times. Experimental results demonstrate that our 3E significantly improves the scheduling quality of the others, and is able to effectively enhance the system elasticity.