期刊
IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS
卷 19, 期 7, 页码 4519-4534出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TWC.2020.2984599
关键词
Task analysis; Servers; Delays; Wireless communication; Computational modeling; Optimization; Edge computing; Internet of Things (IoT); network association; event-triggered; Markov decision process (MDP); Lyapunov optimization; distributed computing; machine learning; mobile edge computing
资金
- National Natural Science Foundation of China [61971066, 61941114, 61790553]
- Beijing Natural Science Foundation [L182038]
- National Youth Top-notch Talent Support Program
- Cyber Florida
Ultra-low latency communication for mobile intelligent machines, such as autonomous vehicles and robots, is a central technology in Internet of Things (IoT) to achieve system reliability. Proactive network association and communication has been suggested to achieve ultra-low latency under the assistance of mobile edge computing. Highly dynamic and stochastic nature of IoT mobile machines suggests applying machine learning methodology to effectively enhance the proactive network association. In this paper, an online proactive network association is proposed for this distributed computing and networking scenario, in order to minimize the average task delay subject to time-average energy consumption. We first formulate an event-triggered delay model for mobility-aware anticipatory network association mechanism that takes future possible handovers into account. Based on the Markov decision processes (MDP) and Lyapunov optimization, a two-stage online decision algorithm for proactive network association is innovated for individual mobile machine without the statistical knowledge of random events that may lack of enough prior data. Theoretical analysis proves that the delay performance of proposed algorithm attains asymptotic optimality within the bounded deviation. Furthermore, an asynchronous online distributed association decision algorithm based on the nonlinear problem transformation is proposed to support more general scenarios of multi-machine event-triggered associations. Simulations verify the effectiveness of the proposed methodology.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据