Joint Task Offloading and Resource Allocation for Multihop Industrial Internet of Things

被引:14
作者
Xu, Jincheng [1 ,2 ]
Yang, Bo [3 ,4 ]
Liu, Yuxiang [3 ,4 ]
Chen, Cailian [3 ,4 ]
Guan, Xinping [3 ,4 ]
机构
[1] Shanghai Jiao Tong Univ, Dept Automat, Shanghai 200240, Peoples R China
[2] Pinduoduo Inc, Res & Dev Dept, Shanghai 200051, Peoples R China
[3] Shanghai Jiao Tong Univ, Minist Educ China, Dept Automat, Key Lab Syst Control & Informat Proc, Shanghai, Peoples R China
[4] Shanghai Jiao Tong Univ, Shanghai Engn Res Ctr Intelligent Control & Manag, Shanghai 200240, Peoples R China
基金
中国国家自然科学基金;
关键词
Task analysis; Computational modeling; Servers; Industrial Internet of Things; Wireless communication; Delays; Routing; Alternating direction method of multiplier (ADMM); edge computing; multihop transmission; resource allocation; task offloading; EDGE; OPTIMIZATION; FOG;
D O I
10.1109/JIOT.2022.3181821
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Task offloading in edge computing is important for the Industrial Internet of Things (IIoT) to implement computation-intensive applications in real time. However, achieving efficient task offloading in IIoT is very challenging due to the limited computing resources of IIoT devices, the coupling of computing and communication resources, and the unreliability in multihop wireless transmission. In this article, we construct a link model by considering the influence of unreliable links in multihop transmission to reveal the relationship between reliability and transmission delay. Then, a nonconvex optimization problem that minimizes task processing delay is formulated, and task offloading is decided by considering transmission path selection, bandwidth allocation, and computational resource allocation. To solve this problem, an algorithm based on the alternating direction method of multipliers (ADMM) is designed using auxiliary variables and reformulation linearization technology (RLT). The simulation results show that our proposed algorithm can fully utilize the computing power of the edge server and reduce the task processing delay. Compared with the centralized algorithms, the performance of the proposed scheme is only 1% worse, but the calculation time can be reduced by 40%.
引用
收藏
页码:22022 / 22033
页数:12
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