Multisatellite intelligent computing, aimed at autonomously generating resource allocation schemes without reliance on ground control, is a crucial technology for the intelligent and integrated Internet of Things (IoT) from space to ground. Existing work focuses on ground-based computation for scheduling schemes, and while some recent studies have adopted graph computing and distributed computing strategies on satellites, they have overlooked the frequent changes in satellite network topologies and the coupling relationships between various types of resources. The robustness advantage of game theory offers a possibility for reliable computation in resource allocation. We introduce a multisatellite intelligent negotiation mechanism based on cooperative game theory, which considers dynamic and autonomous negotiation methods for satellite agents with various types of resources. Subsequently, we propose an intelligent computing algorithm for resource allocation based on convex games and design heuristic strategies for adaptively updating resource allocation schemes. Our simulation results indicate that this method outperforms benchmark algorithms in different scenarios, reducing time consumption by at least 0.151%.

Multi-satellite collaborative computing has achieved task decomposition and collaborative execution through inter-satellite links (ISLs), which has significantly improved the efficiency of task execution and system responsiveness. However, existing methods focus on single-task execution and lack multi-task parallel processing capability. Most methods ignore task priorities and dependencies, leading to excessive waiting times and poor scheduling results. To address these problems, this paper proposes a task decomposition and resource mapping method based on task priorities and resource constraints. First, we introduce a graph theoretic model to represent the task dependency and priority relationships explicitly, combined with a novel algorithm for task decomposition. Meanwhile, we construct a resource allocation model based on game theory and combine it with deep reinforcement learning to achieve resource mapping in a dynamic environment. Finally, we adopt the theory of temporal logic to formalize the execution order and time constraints of tasks and solve the dynamic scheduling problem through mixed-integer nonlinear programming to ensure the optimality and real-time updating of the scheduling scheme. The experimental results demonstrate that the proposed method improves resource utilization by up to about 24% and reduces overall execution time by up to about 42.6% in large-scale scenarios.

Multi-hop inter-domain wireless networks play a vital role in future heterogeneous communication systems by improving data transmission efficiency and security assurance. Despite the advances in secure routing techniques in areas such as node authentication and encryption, they still suffer from the shortcomings of frequent key updates, high computational overhead, and poor adaptability to large-scale dynamic topologies. To address these limitations, we propose a new routing method—the Secure Cross-Layer Route—designed for multi-hop inter-domain wireless networks to achieve unified optimization of security, delay, and throughput. First, we construct a multi-objective optimization model that integrates authentication delay, link load, and resource states, enabling balanced trade-offs between security and transmission performance in dynamic conditions. Second, we introduce a cross-layer information fusion mechanism that allows nodes to adapt routing costs in real time under heterogeneous network conditions, thereby improving path reliability and load balancing. Furthermore, a risk-aware dynamic key update strategy is developed to handle behavioral uncertainty among nodes, reducing authentication overhead and enhancing attack resilience. Experimental evaluations conducted on four datasets with varying network scales demonstrate the superior performance of the proposed method. Experimental results demonstrated that the proposed method achieves at least 28% improvement in effective throughput, reduces average authentication delay by approximately 30%, and increases the secure link ratio by at least 10%, outperforming mainstream routing algorithms under multi-constraint conditions.

Network flow task management involves the efficient allocation and scheduling of data flow tasks within dynamic satellite networks, aiming to effectively address frequent changes in network topology and dynamic traffic fluctuations. Existing research primarily emphasizes traffic prediction and scheduling using spatiotemporal models and machine learning. However, these approaches often depend on extensive historical data for training, making real-time adaptation to rapidly changing network topologies and traffic patterns challenging in dynamic satellite environments. Additionally, their high computational complexity and slow convergence rates hinder their efficiency in large-scale networks. To address these issues, this paper proposes a collaborative optimization framework based on Coding Multi-Path Theory (CMPT). The framework utilizes a Nash bargaining game model to simulate resource competition among the different participants, ensuring fair resource distribution and load balancing. It also integrates real-time network state monitoring with optimization algorithms, within a multi-path scheduling strategy, enabling the dynamic selection of optimal transmission paths to accommodate frequent network topology changes and traffic variations. Experimental results indicate that the proposed method reduced resource allocation task execution time by at least 18.03% compared to traditional methods and enhanced task scheduling efficiency by at least 14.01%. Although CMPT exhibited a slightly higher task latency on certain small-scale datasets compared to some baseline algorithms, its performance remains exceptional in large-scale and high-dimensional scenarios.

The purpose of constructing onboard observation mission queues is to improve the execution efficiency of onboard tasks and reduce energy consumption, representing a significant challenge in achieving efficient global military reconnaissance and target tracking. Existing research often focuses on the aspect of task scheduling, aiming at optimizing the efficiency of single-task execution, while neglecting the complex dependencies that might exist between multiple tasks and payloads. Moreover, traditional task scheduling schemes are no longer suitable for large-scale tasks. To effectively reduce the number of tasks within the network, we introduce a network aggregation graph model based on multiple satellites and tasks, and propose a task aggregation priority dynamic calculation algorithm based on graph computations. Subsequently, we present a dynamic merging-based method for multi-satellite, multi-task aggregation, a novel approach for constructing onboard mission queues that can dynamically optimize the task queue according to real-time task demands and resource status. Simulation experiments demonstrate that, compared to baseline algorithms, our proposed task aggregation method significantly reduces the task size by approximately 25% and effectively increases the utilization rate of onboard resources.