An Energy-Aware Approach for Industrial Internet of Things in 5G Pervasive Edge Computing Environment
An Energy-Aware Approach for Industrial Internet of Things in 5G Pervasive Edge Computing Environment
复制标题
5G 普及边缘计算环境中工业物联网的能源感知方法
DOI:
10.1109/tii.2020.3007973
复制
发表时间:
2021-07
影响因子:
12.3
通讯作者:
Liu Xing
中科院分区:
文献类型:
--
作者:
Chen Qimei;Xu Xiaoxia;Jiang Hao;Liu Xing
Driven by the rapid technological advances, industrial Internet of Things (IIoT) has recently been embraced to enhance autonomous industrial processes. Since a huge diverse traffic would be generated by IIoT, the industrial processes would meet the challenges of spectrum scarcity and on-demand service requirements. Millimeter wave (mmW) and pervasive edge computing (PEC) technologies in 5G communication are available to deal with these requirements. In this article, a novel dual-band framework that integrates both mmW and microwave (<inline-formula><tex-math notation="LaTeX">$\mu$</tex-math></inline-formula>W) networks in PEC environment has been proposed, which locally performs joint resource allocation and power assignment over mmW and <inline-formula><tex-math notation="LaTeX">$\mu$</tex-math></inline-formula>W to meet IIoT devices’ specific requirements. To consider the new prominent figure of merit in IIoT scenario, the scheduling problem is formulated as an optimization problem to minimize the IIoT energy consumption in real-time environment. A Lyapunov optimization technique has been applied for the objective function with low complexity and rapid convergence. To solve the NP-hard Lyapunov algorithm, we introduce a block coordinate descent method that decompose the Lyapunov problem into two nested subproblems over the mmW and <inline-formula><tex-math notation="LaTeX">$\mu$</tex-math></inline-formula>W networks. An initialization-free semidistributed scheme is proposed in mmW PECs, which not only requires little information exchange via the <inline-formula><tex-math notation="LaTeX">$\mu$</tex-math></inline-formula>W network but also achieves the global optimal solution. Numerical results are shown to demonstrate the effectiveness of our proposed algorithms and confirm our theoretical analyses.
登录
查看更多内容
影响因子:
11.2
作者:
Wu, Dan;Wang, Jinlong;Guizani, Mohsen
通讯作者:
Guizani, Mohsen
影响因子:
10.4
作者:
Yin Rui;Liu Shengli;Yu Gu;ing;Zhang Yanqiong;Chen Qimei
通讯作者:
Chen Qimei
DOI:
10.1007/b98874
发表时间:
2018-09
期刊:
--
影响因子:
--
作者:
J. Nocedal;Stephen J. Wright
通讯作者:
J. Nocedal;Stephen J. Wright
DOI:
10.1109/ccdc.2017.7978647
发表时间:
2016-09
期刊:
2017 29th Chinese Control And Decision Conference (CCDC)
影响因子:
--
作者:
Xinyue Shen;Steven Diamond;Madeleine Udell;Yuantao Gu;Stephen P. Boyd
通讯作者:
Xinyue Shen;Steven Diamond;Madeleine Udell;Yuantao Gu;Stephen P. Boyd
影响因子:
8.3
作者:
H. S. Ghadikolaei;C. Fischione
通讯作者:
H. S. Ghadikolaei;C. Fischione