Skip to main content

Advertisement

SpringerLink
Log in

A novel blockchain federated safety-as-a-service scheme for industrial IoT using machine learning

  • 1219: Multimedia Security Based on Quantum Cryptography and Blockchain
  • Published:
Multimedia Tools and Applications Aims and scope Submit manuscript

Abstract

Blockchains are costly in terms of computing and involve high overhead bandwidth and delays that are not suitable for smart appliances. Enhancing the precision of output, quality, and delivery of data is particularly critical in Machine Learning. The combination of Machine Learning and Blockchain technologies may create accurate results. The Industrial IoT (IIoT), has quickly been established and is getting huge attention in educational areas and manufacturing, but IoT solitude danger and privacy exposures are developing by lack of important security technology. Because blockchain technique’s regionalization and information revelation were planned as a decentralized and distributed method to give assurance security and motivate the development of the IoT and IIoT. The Blockchain Driven Cyber-Physical system (BDCPS) is supported by IoT and cloud services. BDCPS will confirm the statement utilizing the Intelligent Agreements functionality and the trust-less peer-to-peer centrally controlled database showcase by a tiny-scale real-life Blockchain to the IoT system. In this study, a private Blockchain can be run on a separate board system and paralleled to a microcontroller with Smart devices. The suggested system uses blockchain technology to resolve issues such as lightweight, evaporation, warehousing transactions, and shipment time. The data flow of Blockchain is intended to demonstrate the application of machine learning to food traceability. Finally, to extend shelf life, a supply chain employs dependable and accurate data. This paper shows a relevant blockchain and machine learning research that identifies numerous key elements of combining the two technologies such as Blockchain and Machine Learning, including an overview, benefits, and applications.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13

Similar content being viewed by others

References

  1. Ali MS, Vecchio M, Pincheira M, Dolui K, Antonelli F, Rehmani MH (2018) Applications of blockchains in the internet of things: a comprehensive survey. IEEE Communications Surveys & Tutorials 21(2):1676–1717 https://doi.org/10.1109/COMST.2018.2886932

    Article  Google Scholar 

  2. Al-Jaroodi J, Mohamed N (2019) Blockchain in industries: a survey. IEEE Access 7:36500–36515

    Article  Google Scholar 

  3. Alsheikh MA, Lin S, Niyato D, Tan H-P (2014) Machine learning in wireless sensor networks: algorithms, strategies, and applications. IEEE Communications Surveys & Tutorials 16(4):1996–2018

    Article  Google Scholar 

  4. Bahga A (2014) And Vijay Madisetti. Internet of Things: A hands-on approach. Vpt

  5. Bahga A, Madisetti VK (2016) Blockchain platform for industrial internet of things. J Softw Eng Appl 9(10):533–546

    Article  Google Scholar 

  6. Benattia A, Ali M (2008) Convergence of technologies in the machine-to-machine (M2M) Space. In Proceedingsof the IEEE Internationa Conference on Applied Electronics 2008, Pilsen, Czech Republic, 10–11 September 2008, pp 9–12

  7. Betarte G, et al. (2018) "Improving Web application firewalls through anomaly detection." 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA). IEEE

  8. Bkassiny M, Yang L, Jayaweera SK (2012) A survey on machine-learning techniques in cognitive radios. IEEE Communications Surveys & Tutorials 15(3):1136–1159

    Article  Google Scholar 

  9. Brody P, Pureswaran V (2014) Device democracy: saving the future of the internet of things. IBM, September 1.1:15. Available from: https://public.dhe.ibm.com/common/ssi/ecm/gb/en/gbe03620usen/global-business-services-global-business-services-gb-executive-brief-gbe03620usen-2017pdf

  10. Cai X, Geng S, Zhang J, Wu D, Cui Z, Zhang W, Chen J (2021) A sharding scheme-based many-objective optimization algorithm for enhancing security in Blockchain-enabled industrial internet of things. In IEEE Transactions on Industrial Informatics 17(11):7650–7658. https://doi.org/10.1109/TII.2021.3051607

  11. Casino F, Dasaklis TK, Patsakis C (2019) A systematic literature review of blockchain-based applications: Current status, classification and open issues. Telematics Inform 36:55–81. https://doi.org/10.1016/j.tele.2018.11.006

  12. Chaudhary K, Alam M, Al-Rakhami MS et al (2021) Machine learning-based mathematical modelling for prediction of social media consumer behavior using big data analytics. J Big Data 8:73. https://doi.org/10.1186/s40537-021-00466-2

  13. Chen X, Yu L, Wang T, Liu A, Wu X, Zhang B, Lv Z, Sun Z (2020) Artificial intelligence-empowered path selection: a survey of ant Colony optimization for static and Mobile sensor networks. IEEE Access 8:71497–71511

    Article  Google Scholar 

  14. Colombo AW et al (2014) Industrial cloud-based cyber-physical systems. The Imc-aesop Approach 22:4–5

    Google Scholar 

  15. Conti M, Sandeep Kumar E, Lal C, Ruj S (2018) A survey on security and privacy issues of bitcoin. IEEE Communications Surveys & Tutorials 20(4):3416–3452. https://doi.org/10.1109/COMST.2018.2842460

    Article  Google Scholar 

  16. Feki MA, Kawsar F, Boussard M, Trappeniers L (2013) The internet of things: the next technological revolution. Computer 46(2):24–25

    Article  Google Scholar 

  17. Jameel F, Javaid U, Khan WU, Aman MN, Pervaiz H, Jäntti R (2020) Reinforcement Learning in Blockchain-Enabled IIoT Networks: a Survey of Recent Advances and Open Challenges. Sustainability 12(12):5161. https://doi.org/10.3390/su12125161

  18. Khurshid A, Zou X, Zhou W, Caesar M, Godfrey PB (2013) Veriflow: Verifying network-wide invariants in real time. Presented as Part of the 10th USENIX Symposium on Networked Systems Design and Implementation (NSDI), Lombard, IL, USA, 2–5 April ; pp. 15–27

  19. Konaković-Luković M, Panetta J, Crane K, Pauly M (2018) Rapid deployment of curved surfaces via programmable auxetics. ACM Transactions on Graphics (TOG) 37(4):1–13. https://doi.org/10.1145/3197517.3201373

    Article  Google Scholar 

  20. Krit S-d, Haimoud E (2017) Overview of firewalls: Types and policies: managing windows embedded firewall programmatically. 2017 Int Conf Eng MIS (ICEMIS). IEEE

  21. Kumari A, Tanwar S, Tyagi S, Kumar N, Obaidat MS, Rodrigues JJPC (2019) Fog computing for smart grid systems in the 5G environment: challenges and solutions. IEEE Wirel Commun 26(3):47–53

    Article  Google Scholar 

  22. Kuzmin A, Znak E (2018) Blockchain-base structures for a secure and operate network of semi-autonomous Unmanned Aerial Vehicles. 2018 IEEE International conference on service operations and logistics, and informatics (SOLI). IEEE

  23. Latif S, Idrees Z, Ahmad J, Zheng L, Zou Z (2021) A blockchain-based architecture for secure and trustworthy operations in the industrial internet of things. J Ind Inf Integr 21:100190. https://doi.org/10.1016/j.jii.2020.100190

    Article  Google Scholar 

  24. Lin Q, Wang H, Pei X, Wang J (2019) Food safety traceability system based on blockchain and EPCIS. IEEE Access 7:20698–20707

    Article  Google Scholar 

  25. Malik S, Kanhere SS, Jurdak R (2018) Productchain: scalable blockchain framework to support provenance in supply chains. In: Proceedings of the 2018 IEEE 17th international symposium on network computing and applications (NCA), Cambridge, MA, USA, 1–3 November 2018, pp 1–10. https://doi.org/10.1109/NCA.2018.8548322

  26. Mao Q, Hu F, Hao Q (2018) Deep learning for intelligent wireless networks: a comprehensive survey. IEEE Commun Surv Tutorials 20(4):2595–2621. https://doi.org/10.1109/COMST.2018.2846401

  27. Marcos E, Genovesio A (2017) Interference between space and time estimations: from behavior to neurons. Front Neurosci 11:631

    Article  Google Scholar 

  28. Mazzei D, Baldi G, Fantoni G, Montelisciani G, Pitasi A, Ricci L, Rizzello L (2020) A Blockchain tokenizer for industrial IOT trustless applications. Futur Gener Comput Syst 105:432–445. https://doi.org/10.1016/j.future.2019.12.020

    Article  Google Scholar 

  29. Meng W, Tischhauser EW, Wang Q, Wang Y, Han J (2018) When intrusion detection meets blockchain technology: a review. Ieee Access 6:10179–10188

    Article  Google Scholar 

  30. Mohammadi M, al-Fuqaha A, Sorour S, Guizani M (2018) Deep learning for IoT big data and streaming analytics: a survey. IEEE Communications Surveys & Tutorials 20(4):2923–2960

    Article  Google Scholar 

  31. Mrugalska B, Wyrwicka MK (2017) Towards lean production in industry 4.0. Procedia engineering 182:466–473

    Article  Google Scholar 

  32. Musumeci F, Rottondi C, Nag A, Macaluso I, Zibar D, Ruffini M, Tornatore M (2018) An overview on application of machine learning techniques in optical networks. IEEE Communications Surveys & Tutorials 21(2):1383–1408

    Article  Google Scholar 

  33. Nakamoto S, Bitcoin A (2008) "A peer-to-peer electronic cash system." Bitcoin. –URL: https://bitcoin.org/bitcoin.pdf 4

  34. Namanya AP, et al. (2018) "The world of malware: An overview." 2018 IEEE 6th International Conference on Future Internet of Things and Cloud (FiCloud). IEEE

  35. Pop C et al (2018) Blockchain based decentralized management of demand response programs in smart energy grids. Sensors 18.1:162

    Article  Google Scholar 

  36. Porras P, Shin S, Yegneswaran V, Fong M, Tyson M, Gu G (2012) A security enforcement kernel for OpenFlow networks. In Proceedings of the First Workshop on Hot Topics in Software Defined Networks, Helsinki, Finland, 13–17 August 2012; pp. 121–126

  37. Qin J, Liu Y, Grosvenor R (2016) A categorical framework of manufacturing for industry 4.0 and beyond. Procedia cirp 52:173–178

    Article  Google Scholar 

  38. Rahouti M, Seigle-Murandi F, Steiman R, Eriksson K-E (1989) Metabolism of ferulic acid by Paecilomyces variotii and Pestalotia palmarum. Appl Environ Microbiol 55(9):2391–2398

    Article  Google Scholar 

  39. Saini VK (n.d.) "Trust Based Blockchain Records for Improving Supply Chain Process."

  40. Salah K, Rehman MHU, Nizamuddin N, al-Fuqaha A (2019) Blockchain for AI: review and open research challenges. IEEE Access 7:10127–10149

    Article  Google Scholar 

  41. Salah K, Nizamuddin N, Jayaraman R, Omar M (2019) Blockchain-based soybean traceability in agricultural supply chain. IEEE Access 7:73295–73305

    Article  Google Scholar 

  42. Salman T, Zolanvari M, Erbad A, Jain R, Samaka M (2018) Security services using blockchains: a state of the art survey. IEEE Communications Surveys & Tutorials 21(1):858–880

    Article  Google Scholar 

  43. Shen M, Liu H, Zhu L, Xu K, Yu H, du X, Guizani M (2020) Blockchain-assisted secure device authentication for cross-domain industrial IoT. in IEEE Journal on Selected Areas in Communications 38(5):942–954. https://doi.org/10.1109/JSAC.2020.2980916

    Article  Google Scholar 

  44. Siano P, de Marco G, Rolan A, Loia V (2019) A survey and evaluation of the potentials of distributed ledger technology for peer-to-peer transactive energy exchanges in local energy markets. IEEE Syst J 13(3):3454–3466

    Article  Google Scholar 

  45. Tanwar S, et al (2017) Fog-based enhanced safety management system for miners. 2017 3rd international conference on advances in computing, communication & automation (ICACCA) (Fall). IEEE

  46. Tian F (2016) An agri-food supply chain traceability system for China based on RFID & blockchain technology. In Proceedings of the IEEE 2016 13th International Conference on Service Systems and Service Management (ICSSSM), Kunming, China, 24–26 June, pp 1–6

  47. Tsolakis AC, et al. (2018) "A Secured and Trusted Demand Response system based on Blockchain technologies." Innovations in intelligent systems and applications (INISTA). IEEE, 2018

  48. Ucci D, Aniello L, Baldoni R (2019) Survey of machine learning techniques for malware analysis. Comput Secur 81:123–147 https://doi.org/10.1016/j.cose.2018.11.001

  49. Vora J, et al (2018) Blind signatures based secured e-healthcare system. 2018 International conference on computer, information and telecommunication systems (CITS). IEEE

  50. Welter F, Baker T, Audretsch DB, Gartner WB (2017) Everyday entrepreneurship—A call for entrepreneurship research to embrace entrepreneurial diversity. Enterp Theory Pract 41(3):311–321. https://doi.org/10.1111/etap.12258

  51. Wu D, Rosen DW, Wang L, Schaefer D (2015) Cloud-based design and manufacturing: a new paradigm in digital manufacturing and design innovation. Comput Aided Des 59:1–14

    Article  Google Scholar 

  52. Xie J, Yu FR, Huang T, Xie R, Liu J, Wang C, Liu Y (2018) A survey of machine learning techniques applied to software-defined networking (SDN): research issues and challenges. IEEE Commun Surv Tutorials 21(1):393–430. https://doi.org/10.1109/COMST.2018.2866942

  53. Xie J, Tang H, Huang T, Yu FR, Xie R, Liu J, Liu Y (2019) A survey of blockchain technology applied to smart cities: research issues and challenges. IEEE Commun Surv Tutorials 21(3):2794–2830. https://doi.org/10.1109/COMST.2019.2899617

  54. Xu X (2012) From cloud computing to cloud manufacturing. Robot Comput Integr Manuf 28(1):75–86

    Article  Google Scholar 

  55. Zhang C, Wu J, Long C, Cheng M (2017) Review of existing peer-to-peer energy trading projects. Energy Procedia 105:2563–2568

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Computer Science, Jamia Millia Islamia, New Delhi, India

    Nabeela Hasan & Mansaf Alam

  2. Department of Commerce, Shivaji College, University of Delhi, New Delhi, India

    Kiran Chaudhary

Authors
  1. Nabeela Hasan
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Kiran Chaudhary
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Mansaf Alam
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Nabeela Hasan.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Ethical approval

This article does not contain any studies with human participants or animals performed by any of the authors.

Informed consent

was obtained from all individual participants included in the study.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Hasan, N., Chaudhary, K. & Alam, M. A novel blockchain federated safety-as-a-service scheme for industrial IoT using machine learning. Multimed Tools Appl 81, 36751–36780 (2022). https://doi.org/10.1007/s11042-022-13503-w

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-022-13503-w

Keywords

Search

Navigation