Latency and Energy Optimization for MEC Enhanced SAT-IoT Networks

Latency and Energy Optimization for MEC Enhanced SAT-IoT Networks
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DOI：
10.1109/access.2020.2982356
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发表时间：
2020
期刊：
IEEE Access
影响因子：
3.9
通讯作者：
Gaofeng Cui;Xiaoyao Li;Lexi Xu;Weidong Wang
Gaofeng Cui;Xiaoyao Li;Lexi Xu;Weidong Wang
中科院分区：
计算机科学3区
文献类型：
--
作者：
Gaofeng Cui;Xiaoyao Li;Lexi Xu;Weidong Wang

文献摘要

相似文献

移动的边缘计算（MEC）增强的基于卫星的物联网（SAT-IoT）是基于陆地网络的物联网的重要补充，特别是对于偏远和人口稀少的地区。对于具有多颗卫星和多个卫星网关的MEC增强型SAT-IoT网络，耦合用户关联、卸载决策、计算和通信资源分配应联合优化，以最小化延迟和能量成本。本文将MEC增强型SAT-IoT网络的时延和能量优化问题归结为一个动态混合整数规划问题，该问题很难得到最优解。为了解决这个问题，我们将复杂的问题分解为两个子问题。第一种是采用固定用户关联和卸载决策的计算和通信资源分配，第二种是采用最优资源分配的联合用户关联和卸载。对于资源分配子问题，基于拉格朗日乘子法证明了其最优解的存在性。然后，将第二子问题进一步表述为马尔可夫决策过程（MDP），并基于深度强化学习（DRL）提出了一种具有最优资源分配的联合用户关联和卸载决策（JUAOD-ORA）。仿真结果表明，该方法可以实现更好的长期回报的延迟和能源成本。
Mobile edge computing (MEC) enhanced satellite based internet of things (SAT-IoT) is an important complement for terrestrial networks based IoT, especially for the remote and depopulated areas. For MEC enhanced SAT-IoT networks with multiple satellites and multiple satellite gateways, the coupled user association, offloading decision, computing and communication resource allocation should be jointly optimized to minimize the latency and energy cost. In this paper, the latency and energy optimization for MEC enhanced SAT-IoT networks are formulated as a dynamic mixed-integer programming problem, which is hard to obtain the optimal solutions. To tackle this problem, we decompose the complex problem into two sub-problems. The first one is computing and communication resource allocation with fixed user association and offloading decision, and the second one is joint user association and offloading with optimal resource allocation. For the sub-problem of resource allocation, the optimal solution is proven to be obtained based on Lagrange multiplier method. And then, the second sub-problem is further formulated as a Markov decision process (MDP), and a joint user association and offloading decision with optimal resource allocation (JUAOD-ORA) is proposed based on deep reinforcement learning (DRL). Simulation results show that the proposed approach can achieve better long-term reward in terms of latency and energy cost.