Communication scheduling scheme based on big-data regression analysis and genetic algorithm for cyber-physical factory automation

Chao Chun Chen; Chao Lieh Chen; Che Yang Ciou; Jia Xuan Liu

doi:10.1109/SMC.2016.7844631

Communication scheduling scheme based on big-data regression analysis and genetic algorithm for cyber-physical factory automation

Chao Chun Chen
, Chao Lieh Chen
, Che Yang Ciou
, Jia Xuan Liu

Institute of Manufacturing Information and Systems

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

9 Citations (Scopus)

Abstract

In the Industry 4.0 era, enterprises are eager to add intelligent and cyber-physical technologies to further enhance the factory automation. However, in cyber-physical factory environment, more than hundreds of or thousands of IoT devices could send data at the same time, which affect the completeness of data collection and also diminish the consequent decision correctness. In this work, we proposed a novel communication scheduling scheme based on big-data regression analysis and genetic algorithm for IoT-enabling devices to collect data in cyber-physical factory automation. The basic idea is to discover collection behaviors of IoT devices and apply the extracted behavior in finding optimal communication schedules. Our proposed scheme asks each IoT device moderately utilize the network bandwidth with their in-memory buffer for maximizing the global benefit, rather than only self benefit. Then we conducted experiments to verify and analyze the proposed scheme. The results of the experiments indicate that our proposed scheme successfully achieve the long-term data collection in scenarios of 200 IoT devices working together. This work provides developers useful experiences for creating manufacturing systems of cyber-physical factory automation.

Original language	English
Title of host publication	2016 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2016 - Conference Proceedings
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	2603-2608
Number of pages	6
ISBN (Electronic)	9781509018970
DOIs	https://doi.org/10.1109/SMC.2016.7844631
Publication status	Published - 2017 Feb 6
Event	2016 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2016 - Budapest, Hungary Duration: 2016 Oct 9 → 2016 Oct 12

Publication series

Name	2016 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2016 - Conference Proceedings

Other

Other	2016 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2016
Country/Territory	Hungary
City	Budapest
Period	16-10-09 → 16-10-12

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 9 Industry, Innovation, and Infrastructure

All Science Journal Classification (ASJC) codes

Computer Vision and Pattern Recognition
Artificial Intelligence
Control and Optimization
Human-Computer Interaction

Access to Document

10.1109/SMC.2016.7844631

Cite this

Chen, C. C., Chen, C. L., Ciou, C. Y., & Liu, J. X. (2017). Communication scheduling scheme based on big-data regression analysis and genetic algorithm for cyber-physical factory automation. In 2016 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2016 - Conference Proceedings (pp. 2603-2608). Article 7844631 (2016 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2016 - Conference Proceedings). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/SMC.2016.7844631

Chen, Chao Chun ; Chen, Chao Lieh ; Ciou, Che Yang et al. / Communication scheduling scheme based on big-data regression analysis and genetic algorithm for cyber-physical factory automation. 2016 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2016 - Conference Proceedings. Institute of Electrical and Electronics Engineers Inc., 2017. pp. 2603-2608 (2016 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2016 - Conference Proceedings).

@inproceedings{9840725232f54438afbf1c04df8748eb,

title = "Communication scheduling scheme based on big-data regression analysis and genetic algorithm for cyber-physical factory automation",

abstract = "In the Industry 4.0 era, enterprises are eager to add intelligent and cyber-physical technologies to further enhance the factory automation. However, in cyber-physical factory environment, more than hundreds of or thousands of IoT devices could send data at the same time, which affect the completeness of data collection and also diminish the consequent decision correctness. In this work, we proposed a novel communication scheduling scheme based on big-data regression analysis and genetic algorithm for IoT-enabling devices to collect data in cyber-physical factory automation. The basic idea is to discover collection behaviors of IoT devices and apply the extracted behavior in finding optimal communication schedules. Our proposed scheme asks each IoT device moderately utilize the network bandwidth with their in-memory buffer for maximizing the global benefit, rather than only self benefit. Then we conducted experiments to verify and analyze the proposed scheme. The results of the experiments indicate that our proposed scheme successfully achieve the long-term data collection in scenarios of 200 IoT devices working together. This work provides developers useful experiences for creating manufacturing systems of cyber-physical factory automation.",

author = "Chen, \{Chao Chun\} and Chen, \{Chao Lieh\} and Ciou, \{Che Yang\} and Liu, \{Jia Xuan\}",

note = "Funding Information: Authors thank Jung-Chung Yeh and Hsin-Ting Shih with Tong-Tai Co. for sharing valuable machining experiences in whole factory, which assist us to create the simulation environment of data collection. This work was supported by Ministry of Science and Technology of Taiwan (R.O.C) and Tong-Tai Machine and Tool Co. under Grants MOST 105-2218-E-006-014, 103-2221-E-006-155, and NCKU B104- 018. This research was also received funding from the Headquarters of University Advancement at the National Cheng Kung University, which is sponsored by the Ministry of Education, Taiwan, R.O.C Publisher Copyright: {\textcopyright} 2016 IEEE.; 2016 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2016 ; Conference date: 09-10-2016 Through 12-10-2016",

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doi = "10.1109/SMC.2016.7844631",

language = "English",

series = "2016 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2016 - Conference Proceedings",

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Chen, CC, Chen, CL, Ciou, CY & Liu, JX 2017, Communication scheduling scheme based on big-data regression analysis and genetic algorithm for cyber-physical factory automation. in 2016 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2016 - Conference Proceedings., 7844631, 2016 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2016 - Conference Proceedings, Institute of Electrical and Electronics Engineers Inc., pp. 2603-2608, 2016 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2016, Budapest, Hungary, 16-10-09. https://doi.org/10.1109/SMC.2016.7844631

Communication scheduling scheme based on big-data regression analysis and genetic algorithm for cyber-physical factory automation. / Chen, Chao Chun; Chen, Chao Lieh; Ciou, Che Yang et al.
2016 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2016 - Conference Proceedings. Institute of Electrical and Electronics Engineers Inc., 2017. p. 2603-2608 7844631 (2016 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2016 - Conference Proceedings).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

TY - GEN

T1 - Communication scheduling scheme based on big-data regression analysis and genetic algorithm for cyber-physical factory automation

AU - Chen, Chao Chun

AU - Chen, Chao Lieh

AU - Ciou, Che Yang

AU - Liu, Jia Xuan

N1 - Funding Information: Authors thank Jung-Chung Yeh and Hsin-Ting Shih with Tong-Tai Co. for sharing valuable machining experiences in whole factory, which assist us to create the simulation environment of data collection. This work was supported by Ministry of Science and Technology of Taiwan (R.O.C) and Tong-Tai Machine and Tool Co. under Grants MOST 105-2218-E-006-014, 103-2221-E-006-155, and NCKU B104- 018. This research was also received funding from the Headquarters of University Advancement at the National Cheng Kung University, which is sponsored by the Ministry of Education, Taiwan, R.O.C Publisher Copyright: © 2016 IEEE.

PY - 2017/2/6

Y1 - 2017/2/6

N2 - In the Industry 4.0 era, enterprises are eager to add intelligent and cyber-physical technologies to further enhance the factory automation. However, in cyber-physical factory environment, more than hundreds of or thousands of IoT devices could send data at the same time, which affect the completeness of data collection and also diminish the consequent decision correctness. In this work, we proposed a novel communication scheduling scheme based on big-data regression analysis and genetic algorithm for IoT-enabling devices to collect data in cyber-physical factory automation. The basic idea is to discover collection behaviors of IoT devices and apply the extracted behavior in finding optimal communication schedules. Our proposed scheme asks each IoT device moderately utilize the network bandwidth with their in-memory buffer for maximizing the global benefit, rather than only self benefit. Then we conducted experiments to verify and analyze the proposed scheme. The results of the experiments indicate that our proposed scheme successfully achieve the long-term data collection in scenarios of 200 IoT devices working together. This work provides developers useful experiences for creating manufacturing systems of cyber-physical factory automation.

AB - In the Industry 4.0 era, enterprises are eager to add intelligent and cyber-physical technologies to further enhance the factory automation. However, in cyber-physical factory environment, more than hundreds of or thousands of IoT devices could send data at the same time, which affect the completeness of data collection and also diminish the consequent decision correctness. In this work, we proposed a novel communication scheduling scheme based on big-data regression analysis and genetic algorithm for IoT-enabling devices to collect data in cyber-physical factory automation. The basic idea is to discover collection behaviors of IoT devices and apply the extracted behavior in finding optimal communication schedules. Our proposed scheme asks each IoT device moderately utilize the network bandwidth with their in-memory buffer for maximizing the global benefit, rather than only self benefit. Then we conducted experiments to verify and analyze the proposed scheme. The results of the experiments indicate that our proposed scheme successfully achieve the long-term data collection in scenarios of 200 IoT devices working together. This work provides developers useful experiences for creating manufacturing systems of cyber-physical factory automation.

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U2 - 10.1109/SMC.2016.7844631

DO - 10.1109/SMC.2016.7844631

M3 - Conference contribution

AN - SCOPUS:85015778987

T3 - 2016 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2016 - Conference Proceedings

SP - 2603

EP - 2608

BT - 2016 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2016 - Conference Proceedings

PB - Institute of Electrical and Electronics Engineers Inc.

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ER -

Chen CC, Chen CL, Ciou CY, Liu JX. Communication scheduling scheme based on big-data regression analysis and genetic algorithm for cyber-physical factory automation. In 2016 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2016 - Conference Proceedings. Institute of Electrical and Electronics Engineers Inc. 2017. p. 2603-2608. 7844631. (2016 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2016 - Conference Proceedings). doi: 10.1109/SMC.2016.7844631

Communication scheduling scheme based on big-data regression analysis and genetic algorithm for cyber-physical factory automation

Abstract

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