Collaborative Computing and Resource Allocation for LEO Satellite-Assisted Internet of Things
Collaborative Computing and Resource Allocation for LEO Satellite-Assisted Internet of Things
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DOI：
10.1155/2021/4212548
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发表时间：
2021-09
影响因子：
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通讯作者：
Tao Leng;Xiaoyao Li;Dongwei Hu;Gaofeng Cui;Weidong Wang
中科院分区：
文献类型：
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作者：
Tao Leng;Xiaoyao Li;Dongwei Hu;Gaofeng Cui;Weidong Wang
卫星辅助物联网（S-IoT），特别是基于近地轨道卫星的S-IoT，将在未来的无线系统中发挥重要作用。然而，低轨道卫星有限的星载通信和计算资源以及高移动性使得其难以为物联网用户提供满意的服务。为了在时延约束下最大限度地提高任务完成率，本文将LEO网络间的协同计算和资源分配问题结合起来进行研究，并将联合任务卸载、调度和资源分配问题表述为一个动态混合整数问题。为了解决复杂问题，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优资源分配来最小化最大延迟。然后，将联合任务卸载和调度制定为通信和计算资源分配最优的马尔可夫决策过程，并利用深度强化学习获得长期效益。仿真结果表明，该方案具有较好的性能。
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.