Satellite-assisted internet of things (S-IoT), especially the S-IoT based on low earth orbit (LEO) satellite, plays an important role in future wireless systems. However, the limited on-board communication and computing resource and high mobility of LEO satellites make it hard to provide satisfied service for IoT users. To maximize the task completion rate under latency constraints, collaborative computing and resource allocation among LEO networks are jointly investigated in this paper, and the joint task offloading, scheduling, and resource allocation is formulated as a dynamic mixed-integer problem. To tack the complex problem, we decouple it into two subproblems with low complexity. First, the max-min fairness is adopted to minimize the maximum latency via optimal resource allocation with fixed task assignment. Then, the joint task offloading and scheduling is formulated as a Markov decision process with optimal communication and computing resource allocation, and deep reinforcement learning is utilized to obtain long-term benefits. Simulation results show that the proposed scheme has superior performance compared with other referred schemes.

中文翻译：

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低轨卫星辅助物联网协同计算与资源分配

卫星辅助物联网（S-IoT），特别是基于低地球轨道（LEO）卫星的S-IoT，在未来的无线系统中扮演着重要的角色。然而，低轨卫星的星载通信和计算资源有限，机动性强，难以为物联网用户提供满意的服务。为了在延迟约束下最大化任务完成率，本文联合研究了 LEO 网络之间的协同计算和资源分配，并将联合任务卸载、调度和资源分配制定为一个动态混合整数问题。为了解决复杂的问题，我们将其解耦为两个低复杂度的子问题。首先，采用最大-最小公平性通过固定任务分配的最佳资源分配来最小化最大延迟。然后，联合任务卸载和调度被制定为具有最优通信和计算资源分配的马尔可夫决策过程，并且利用深度强化学习来获得长期收益。仿真结果表明，与其他参考方案相比，所提出的方案具有优越的性能。