.png){kind=icon}
返回
Maximizing Computation Rate for Sustainable Wireless-Powered MEC Network: An Efficient Dynamic Task Offloading Algorithm with User Assistance
.png){kind=icon}
OA
AI
DOI：10.3390/math12162478.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
In the Internet of Things (IoT) era, Mobile Edge Computing (MEC) significantly enhances the efficiency of smart devices but is limited by battery life issues. Wireless Power Transfer (WPT) addresses this issue by providing a stable energy supply. However, effectively managing overall energy consumption remains a critical and under-addressed aspect for ensuring the network's sustainable operation and growth. In this paper, we consider a WPT-MEC network with user cooperation to migrate the double near-far effect for the mobile node (MD) far from the base station. We formulate the problem of maximizing long-term computation rates under a power consumption constraint as a multi-stage stochastic optimization (MSSO) problem. This approach is tailored for a sustainable WPT-MEC network, considering the dynamic and varying MEC network environment, including randomness in task arrivals and fluctuating channels. We introduce a virtual queue to transform the time-average energy constraint into a queue stability problem. Using the Lyapunov optimization technique, we decouple the stochastic optimization problem into a deterministic problem for each time slot, which can be further transformed into a convex problem and solved efficiently. Our proposed algorithm works efficiently online without requiring further system information. Extensive simulation results demonstrate that our proposed algorithm outperforms baseline schemes, achieving approximately 4% enhancement while maintain the queues stability. Rigorous mathematical analysis and experimental results show that our algorithm achieves O(1/V),O(V) trade-off between computation rate and queue stability.
Keyword:
Mobile Edge Computing (MEC)
Wireless Power Transfer (WPT)
computation rate
Lyapunov optimization
convex optimization
期刊
IF:
2.2
论文数:
2.9K
被引数:
3.6W
机构
引用论文
Edge Intelligence: Paving the Last Mile of Artificial Intelligence With Edge Computing边缘智能: 用边缘计算铺平人工智能的最后一英里.png)
PROCEEDINGS OF THE IEEE
OAAI
PROCEEDINGS OF THE IEEEIF25.9
OAAI分享收藏分享收藏Energy-Harvesting Active Networked Tags (EnHANTs): Prototyping and Experimentation能量收集有源网络标签 (EnHANTs): 原型设计和实验
PREAI
分享收藏
查看更多内容