Distributed Scheduling Techniques that Ensure Blockchain-Based IIoT Transparency

doi:10.22716/sckt.2023.11.2.016

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2023 Vol.11, Issue 2 Preview Page Next Page

Research Article

Distributed Scheduling Techniques that Ensure Blockchain-Based IIoT Transparency 블록체인 기반의 IIoT 투명성을 보장하는 분산 스케쥴링 기법: Yoon-Su Jeong¹
정윤수¹; ¹Professor, Dept. of information Communication Engineering, Mokwon University

¹목원대학교 정보통신공학과 교수

30 June 2023. pp. 57-66

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Abstract

IoT 기술과 인공지능 기술이 접목되면서 중소기업을 중심으로 사용하였던 IIoT 기술이 대기업을 중심으로 사용 범위가 확대되고 있다. IIoT 기술은 센서로부터 수집된 정보를 서버로 전달할 때, IIoT 정보가 훼손될 경우, 제조 과정에서 제품의 불량률 및 생산 비용이 증가하는 문제가 발생한다. 본 연구에서는 에지 환경의 스마트 제조 과정에서 생산되는 제품의 품질 및 생산 비용을 줄이기 위한 블록체인 기반의 효율적인 IIoT 분산 스케쥴링 방법을 제안한다. 제안 방법은 생산 제품별 중요 정보(장치별 거리, 위치, 잔존 에너지 등)를 제품 생산 기간 동안 유지하면서 사전 정의된 한계점과 비교하여 오류가 발생한 생산 제품을 사전에 선별하여 제품 생산 비용을 줄이는데 목적이 있다. 제안 방법은 IIoT 장치의 위치 에러 기준을 정할 때, 수신 신호 강도를 사전 정의된 한계점에 적용하여 분산 스케쥴링에 할당하고 IIoT 장치는 동작 유·무에 따라 active/unactive 로 설정한다.

With the combination of IoT technology and artificial intelligence technology, IIoT technology, which was used mainly by mid-term companies, is expanding its scope of use mainly by large companies. When IIoT technology delivers information collected from sensors to servers, if IIoT information is damaged, there is a problem that the defect rate and production cost of the product increase during the manufacturing process. This study proposes an efficient blockchain-based IIoT distributed scheduling method to reduce the quality and production cost of products produced during smart manufacturing in an edge environment. The proposed method aims to reduce production costs by selecting the product that has failed in advance compared to the predefined threadhold while maintaining important information for each product (distance by device, location, residual energy, etc.) during product product production. The proposed method applies the received signal strength to the predefined threshold and allocates it to distributed scheduling when setting the location error criterion of the IIoT device, and the IIoT device is set to active/inactive depending on whether or not it operates.

Keywords

Distributed scheduling

Transparency

Blockchain

IIoT

Smart manufacturing

Production costs

References

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Information

Publisher :The Society of Convergence Knowledge
Publisher(Ko) :융복합지식학회
Journal Title :The Society of Convergence Knowledge Transactions
Journal Title(Ko) :융복합지식학회논문지
Volume : 11
No :2
Pages :57-66
DOI :https://doi.org/10.22716/sckt.2023.11.2.016

[1] J. S. Lee, "A Study on the Effects of the Cooperative Philosophy between SMEs to the Cooperative Activities and Performance", Journal of the Korea Convergence Society, Vol. 8, No. 9, pp. 301-309, 2017.

[2] C. Garrido-Hidalgo, D. Hortelano, L. Roda-Sanchez, T. Olivares, M. C. Ruiz, and V. Lopez, "Iot heterogeneous mesh network deployment for human-in-the-loop challenges towards a social and sustainable industry 4.0", IEEE Access, Vol. 6, pp. 28417-28437, 2018. 10.1109/ACCESS.2018.2836677

[3] J. Dugdale, M. T. Moghaddam, and H. Muccini, "Iot4emergency: Internet of things for emergency management", ACM SIGSOFT Software Engineering Notes, Vol. 46, No. 1, pp. 33-36, 2021. 10.1145/3437479.3437489

[4] M. T. Moghaddam, E. Rutten, P. Lalanda, and G. Giraud, "Ias: an iot architectural self-adaptation framework", in European Conference on Software Architecture, Springer, pp. 333-351, 2020. 10.1007/978-3-030-58923-3_22

[5] F. R. Yu, J. M. Liu , Y. He, P. B. Si, and Y. H. Zhang, "Virtualization for distributed ledger technology (VDLT)," IEEE Access, Vol. 6, pp. 25019-25028, 2018. 10.1109/ACCESS.2018.2829141

[6] D. Miller, "Blockchain and the Internet of Things in the industrial sector", IT Professional, Vol. 20, No. 3, pp. 15-18, 2018. 10.1109/MITP.2018.032501742

[7] X. Liang, J. Zhao, S, Shetty, and D. Li, "Towards data assurance and resilience in IoT using blockchain", Proceedings of the IEEE Military Communications Conference, pp. 261-266, 2017. 10.1109/MILCOM.2017.8170858

[8] R. Davies, "Industry 4.0 digitalisation for productivity and growth", European Parliament PE 568.337, Eur. Parliamentary Res. Service, Vol. 1, 2015, https://www.europarl.europa.eu/RegData/etudes/BRIE/2015/568337/EPRS_BRI(2015)568337_EN.pdf

[9] F. Shrouf, J. Ordieres, and G. Miragliotta, "Smart factories in Industry 4.0: A review of the concept and of energy management approached in production based on the Internet of Things paradigm", in Proc. IEEE Int. Conf. Ind. Eng. Manage., pp. 697-701, 2014. 10.1109/IEEM.2014.7058728

[10] J. Wu, S. Guo, H. Huang, W. Liu, and Y. Xiang, "Information and communications technologies for sustainable development goals: Stateof-the-art, needs and perspectives", IEEE Commun. Surveys Tuts., Vol. 20, No. 3, pp. 2389-2406, 3rd Quart., 2018. 10.1109/COMST.2018.2812301

[11] S. Weyer, M. Schmitt, M. Ohmer, and D. Gorecky, "Towards industry 4.0-standardization as the crucial challenge for highly modular, multi-vendor production systems", IFAC-Papersonline, Vol. 48, No. 3, pp. 579-584, 2015. 10.1016/j.ifacol.2015.06.143n>

[12] S. Kianoush, S. Savazzi, F. Vicentini, V. Rampa and M. Giussani, "Device-Free RF Human Body Fall Detection and Localization in Industrial Workplaces", IEEE Internet of Things Journal, Vol. 4, No. 2, pp. 351-362, 2016. 10.1109/JIOT.2016.2624800

[13] Y. Tian, T.-M. Choi, X. Ding, R. Xing and J. Zhao, "A grid cumulative probability localization-based industrial risk monitoring system", IEEE Transactions on Automation Science and Engineering, Vol. 16, No. 2, pp. 557-569, 2018. 10.1109/TASE.2018.2839194

[14] J. M. Batalla, C. X. Mavromoustakis, G. Mastorakis, N. N. Xiong and J. Wozniak, "Adaptive positioning systems based on multiple wireless interfaces for industrial IoT in Harsh manufacturing environments", IEEE Journal on Selected Areas in Communications, Vol. 38, No. 5, pp. 899-914, 2020. 10.1109/JSAC.2020.2980800

[15] R. Zhao, X. Wang, J. Xia and L. Fan, "Deep reinforcement learning based mobile edge computing for intelligent Internet of things", Phys. Commun., Vol. 43, pp. 161-184, 2020. 10.1016/j.phycom.2020.101184

[16] H. Tang, H. Wu, G. Qu and R. Li, "Double deep q-network based dynamic framing offloading in vehicular edge computing", IEEE Trans. Netw. Sci. Eng., 2022. 10.1109/TNSE.2022.3172794

[17] O. Lohse and N. Pütz, K. Hörmann, "Implementing an online scheduling approach for production with multi agent proximal policy optimization (MAPPO)", IFIP Adv. Inf. Commun. Technol., Vol. 634, pp. 586-595, 2021. 10.1007/978-3-030-85914-5_62

[18] Z. Wang, D. Sun, G. Xue, S. Qian, G. Li and M. Li, "Ada-things: an adaptive virtual machine monitoring and migration strategy for Internet of things applications", J. Parallel Distrib. Comput., Vol. 132, pp. 164-176, 2019. 10.1016/j.jpdc.2018.06.009

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