Automation, Control and Intelligent Systems
Volume 7, Issue 1, February 2019, Pages: 25-38
Received: Feb. 16, 2019;
Accepted: Apr. 16, 2019;
Published: May 26, 2019
Views 252 Downloads 27
Barco You, Institute of Computer Engineering, Heidelberg University, Heidelberg, Germany; Department of IoT, Dasudian Technologies Ltd., Shenzhen, China
Matthias Hub, Department of AI, Dasudian GmbH, Stuttgart, Germany
Ivan Uemlianin, Department of IoT, Dasudian Technologies Ltd., Shenzhen, China
The manufacturing industry featured centralization in the past due to technical limitations, and factories (especially large manufacturers) gathered almost all of the resources for manufacturing, including: technologies, raw materials, equipment, workers, market information, etc. However, such centralized production is costly, inefficient and inflexible, and difficult to respond to rapidly changing, diverse and personalized user needs. This paper introduces an Intelligent Industrial Network (BCIIN), which provides a fully distributed manufacturing network where everyone can participate in manufacturing due to decentralization and no intermediate links, allowing them to quickly get the products or services they want and also to be authorized, recognized and get returns in a low-cost way due to their efforts (such as providing creative ideas, designs or equipment, raw materials or physical strength). BCIIN is a blockchain based IoT and AI technology platform, and also an IoT based intelligent service standard. Due to the intelligent network formed by BCIIN, the manufacturing center is no longer a factory, and actually there are no manufacturing centers. BCIIN provides a multi-participation peer-to-peer network for people and things (including raw materials, equipment, finished / semi-finished products, etc.). The information transmitted through the network is called Intelligent Service Algorithm (ISA). The user can send a process model, formula or control parameter to a device via an ISA, and every transaction in BCIIN is an intelligent service defined by ISA.
Block Chain Based Intelligent Industrial Network (BCIIN), Automation, Control and Intelligent Systems.
Vol. 7, No. 1,
2019, pp. 25-38.
W. S. Shi, J. Cao, and Q. Zhang et al., “Edge Computing: Vision and Challenges,” IEEE Internet of Things Journal, vol. 3, no. 5, pp. 637–646, Oct. 2016.
P. A. Sarvari, A. Ustundag, E. Cevikcan, I. Caya and S. Cebi, Industry 4.0: Managing the Digital Transformation, Springer Series. Springer Series in Advanced Manufacturing. Springer, Cham, 2018.
S. Vongsingthong, S. Smanchat, “Internet of Things: A Review of applications & technologies,” Suranaree Journal of Science and Technology, 2014.
J. Greenough, The Enterprise Internet of Things Report: Forecasts, Industry Trends, Advantages, and Barriers for The Top IoT Sector, Business Insider, Inc., Nov. 2014.
C. Perera, C. H. Liu and S. Jayawardena, “The Emerging Internet of Things Marketplace from an Industrial Perspective: A Survey,” IEEE Transactions on Emerging Topics in Computing, vol. 3, no. 4, pp. 585-598, Dec. 2015.
C. Alippi, Intelligence for Embedded Systems, Springer International Publishing, Springer, Cham, 2014.
F. C. Delicato, A. Al-Anbuky and K. Wang, “Smart Cyber-Physical Systems: towards Pervasive Intelligence systems,” Future Generation Computer Systems. Elsevier. July 2018.
C. Yang, W. M. Shen, X. B. Wang, “The Internet of Things in Manufacturing: Key Issues and Potential Applications,” IEEE Systems, Man, and Cybernetics Magazine, vol. 4, no. 1, pp. 6-15, Jan. 2018.
P. Daugherty, P. Banerjee, W. Negm and A. E. Alter, “Driving Unconventional Growth through the Industrial Internet of Things,” Accenture Technology, Mar. 2016.
J. Lee, B. Bagheri, H. A. Kao, “Recent Advances and Trends of Cyber-Physical Systems and Big Data Analytics in Industrial Informatics,” IEEE Int. Conference on Industrial Informatics (INDIN), 2014.
J. Lee, E. Lapira, B. Bagheri, H. A. Kao, “Recent advances and trends in predictive manufacturing systems in big data environment,” Manufacturing Letters, vol. 1, no. 1, pp. 38-41, 2013.
J. Lee, B. Bagheri and H. A. Kao, “A cyber-physical systems architecture for industry 4.0-based manufacturing systems,” Manufacturing Letters, vol. 3, pp. 18-23, 2015.
S. Severi, G. Abreu, F. Sottile, C. Pastrone, M. Spirito, and F. Berens, “M2M Technologies: Enablers for a Pervasive Internet of Things,” The European Conference on Networks and Communications (EUCNC2014), June 2014.
K. Schwab, The Fourth Industrial Revolution, World Economic Forum, ISBN: 1944835008, Jan. 2016.
S. Nakamoto, “Bitcoin: A peer-to-peer electronic cash system,” 2008. [Online]. Available: https://bitcoin.org/bitcoin.pdf.
V. Buterin, “Ethereum: A next-generation smart contract and decentralized application platform,” 2014. [Online]. Available: https://github.com/ethereum/wiki/wiki/White-Paper.
P. Sztorc, “Market empiricism,” [Online]. Available: http://bitcoinhivemind.com/papers/1_ Purpose.pdf.
C. Detrio, “Smart markets for smart contracts,” 2015. [Online]. Available: http://cdetr.io/smartmarkets/.
Namecoin wiki, 2016. [Online]. Available: https://wiki.namecoin.org/index.php?title=Welcome.
P. Snow, B. Deery, J. Lu, et al., “Factom: Business processes secured by immutable audit trails on the blockchain,” 2014. [Online]. Available: http:// bravenewcoin.com/assets/Whitepapers/ Factom-Whitepaper.pdf.
J. Armstrong, “A history of Erlang,” in HOPL III: Proceedings of the third ACM SIGPLAN conference on History of programming languages, pp. 6–1, doi:10.1145/1238844.1238850, ISBN 978-1-59593-766-7, 2007.
J. Armstrong, “Erlang,” Communications of the ACM, vol. 53, no. 9, pp. 68-75, Sep. 2010.
J. Tromp, “Cuckoo Cycle: A Memory Bound Graph-Theoretic Proof-of-Work,” In: Brenner M., Christin N., Johnson B., Rohloff K. (eds) Financial Cryptography and Data Security. FC 2015. Lecture Notes in Computer Science, vol 8976. Springer, Berlin, Heidelberg.
C. Hewitt, P. Bishop and R. Steiger, “A Universal Modular Actor Formalism for Artificial Intelligence,” IJCAI, 1973.
G. Agha, “Actors: A Model of Concurrent Computation in Distributed Systems,” Doctoral Dissertation. MIT Press. 1986.
G. Agha, I. Mason, S. Smith and C. Talcott, “A Foundation for Actor Computation,” Journal of Functional Programming, Jan. 1993.
F. Cesarini, S. Thompson, OTP Behaviors. In Erlang Programming, O'Reilly Media, June 2009.
W. Loder, Erlang and Elixir for Imperative Programmers. In Chapter 16: Code Structuring Concepts, section title Actor Model, Leanpub, July 2015.
K. Kruger, A. Basson, Erlang-Based Holonic Controller for a Modular Conveyor System. In: Borangiu T., Trentesaux D., Thomas A., Leitão P., Oliveira J. (eds) Service Orientation in Holonic and Multi-Agent Manufacturing. SOHOMA 2016. Studies in Computational Intelligence, vol 694. Springer, Cham, 2017.
J. Peterson and J. Krug, “Augur: A decentralized, open-source platform for prediction markets,” 2014. [Online]. Available: https://bravenewcoin.com/assets/Whitepapers/Augur-A-Decentralized-Open-Source-Platform-for-Prediction-Markets.pdf.
R. Hahn, and V. Smith, et al. “The Promise of Prediction Markets,” Science, vol. 320, pp. 877-78, 2008.
S. Z. Yu, “Hidden Semi-Markov Models,” Artificial Intelligence, vol. 174, no. 2, pp. 215–243, doi:10.1016/j.artint.2009.11.011, 2009.
S. Z. Yu, Hidden Semi-Markov Models: Theory, Algorithms and Applications, 1st Edition, 208 pages, Publisher: Elsevier, Nov. 2015 ISBN 978-0128027677.