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| Computational offloading in D2D-MEC with energy harvesting |
Yaoping ZENG( ),Yueqiang LIU,Saishen GUAN,Weiwei JIANG,Yuting XIA |
| School of Communications and Information Engineering, Xi'an University of Posts and Telecommunications, Xi'an 710121, China |
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Abstract The task offloading and resource allocation problems within D2D-MEC internet of things, which incorporates social relationships and energy harvesting (EH), were analyzed aiming at the energy consumption and security in mobile edge computing (MEC). An online decision matching and resource allocation (ODMRA) algorithm was proposed based on Lyapunov optimization for D2D communication. Social relationships among users were quantified into a social trust matrix. Energy consumption, packet loss and social trustworthiness were articulated as a long-term stochastic optimization problem. The Lyapunov optimization technique was employed to decompose this into a series of sub-problems, which were then solved individually. A low-complexity strategy selection algorithm was designed by combining submodular optimization and greedy algorithms for the decision-making sub-problems between D2D pairs. Theoretical analysis and simulation results showed that the proposed ODMRA algorithm effectively optimized the offloading scheme, balanced the system service cost and queue length, and outperformed other comparative algorithms in terms of energy consumption and system service cost.
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Received: 27 April 2023
Published: 26 April 2024
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| Fund: 陕西省重点研究开发项目(2024NC-YBXM-206). |
能量收集下的D2D-MEC计算卸载
针对移动边缘计算(MEC) 在能源消耗和安全性方面的问题，研究具有社会关系和能量收集(EH)的D2D-MEC物联网网络中的任务卸载和资源分配问题，提出基于李雅普诺夫优化的D2D在线决策匹配和资源分配(ODMRA)算法. 将用户之间的社会关系量化为社会信任矩阵，将能源消耗、包丢失、社会信任度表述为长期随机优化问题，采用李雅普诺夫优化方法将其分解为一系列子问题后分别求解. 对于D2D间的决策选择子问题，结合子模块优化和贪婪算法设计低复杂度的策略选择算法. 理论分析和仿真结果表明，所提出的ODMRA算法有效地优化了卸载方案，平衡了系统服务成本和队列长度，在能量消耗、系统服务成本方面优于其他对比算法.
关键词：
移动边缘计算,
设备对设备,
能量收集,
李雅普诺夫优化,
子模块优化
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