Efficient Bandwidth Allocation and Computation Configuration in Industrial IoT

doi:10.12142/ZTECOM.202301007

ZTE Communications ›› 2023, Vol. 21 ›› Issue (1): 55-63.DOI: 10.12142/ZTECOM.202301007

• Research Paper • Previous Articles

Efficient Bandwidth Allocation and Computation Configuration in Industrial IoT

HUANG Rui, LI Huilin, ZHANG Yongmin()

Central South University, Changsha 410012, China

Received:2022-12-01 Online:2023-03-25 Published:2024-03-15
About author:HUANG Rui received his BS degree in computer science from Wuhan University of Technology, China. He is currently pursuing his master's degree with the School of Computer Science and Engineering, Central South University, China. His research interests include mobile edge computing and network optimization.
LI Huilin received his BS degree in mechanical design manufacture and automation from Shandong University, China. He is currently pursuing his master's degree with the School of Computer Science and Engineering, Central South University, China. His research interests include mobile edge computing and federal learning.
ZHANG Yongmin (zhangyongmin@csu.edu.cn) received his PhD degree in control science and engineering from Zhejiang University, China in 2015. From 2015 to 2019, he was a post-doctoral research fellow at the Department of Electrical and Computer Engineering, University of Victoria, Canada. He is currently a professor with the School of Computer Science and Engineering, Central South University, China. His research interests include resource management and optimization in wireless networks, smart grid, and mobile computing. He won the Best Paper Award of the IEEE PIMRC'12 and the IEEE Asia-Pacific Outstanding Paper Award 2018.
Supported by:
This work has been supported in part by the National Natural Science Foundation of China(62172445);the Young Talents Plan of Hunan Province, China

Abstract

Abstract:

With the advancement of the Industrial Internet of Things (IoT), the rapidly growing demand for data collection and processing poses a huge challenge to the design of data transmission and computation resources in the industrial scenario. Taking advantage of improved model accuracy by machine learning algorithms, we investigate the inner relationship of system performance and data transmission and computation resources, and then analyze the impacts of bandwidth allocation and computation resources on the accuracy of the system model in this paper. A joint bandwidth allocation and computation resource configuration scheme is proposed and the Karush-Kuhn-Tucker (KKT) conditions are used to get an optimal bandwidth allocation and computation configuration decision, which can minimize the total computation resource requirement and ensure the system accuracy meets the industrial requirements. Simulation results show that the proposed bandwidth allocation and computation resource configuration scheme can reduce the computing resource usage by 10% when compared to the average allocation strategy.

Key words: bandwidth allocation, computation resource management, industrial IoT, system accuracy

HUANG Rui, LI Huilin, ZHANG Yongmin. Efficient Bandwidth Allocation and Computation Configuration in Industrial IoT[J]. ZTE Communications, 2023, 21(1): 55-63.

Figures/Tables 6

Figure 1 An example of industrial scenarios

Figure 2 Impact of computation resources and data set on the accuracy of training results

Figure 3 Impact of distance from IoT devices to AP on the actual transmission rate and that of data set on computation resources

Figure 4 Impact of bandwidth resources allocated to IoT device on data set and computation resources

Figure 5 CDF of computation resource requirement of each IoT device and total computation resources requirement under two situations: 1) optimal bandwidth resources allocation decided by EBACC; 2) allocating bandwidth resources equally to all IoT devices.

Figure 6 Relationship between the optimal bandwidth allocation decision and distances of IoT devices

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Efficient Bandwidth Allocation and Computation Configuration in Industrial IoT

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