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A blockchain-based IoT data management scheme using Bernoulli distribution convergence in the mobile edge computing

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Abstract

With the creation of a mobile edge computing environment in which IoT technologies are converged on cloud services, the importance of high-capacity data processing technologies is increasing. In this paper, we propose a block chain–based convergence data management technique to efficiently handle different kinds of data processed in mobile edge computing environment. The proposed technique minimized data loss by connecting information from specific IoT devices to multiple hash boxes and adding electronic signatures to the first and last information. In addition, the similarity of IoT data is applied stochastically to respond flexibly to changes in the system. Since the proposed technique links IoT data with different probabilities, it has distributed and deployed IoT data of n bits according to certain rules to reduce the load that can occur on the server. As a result of performance evaluation, the proposed technique has improved the conversion performance assessment of average transcoding propit because block password hash increases depending on AP density and scope when managing blockchain-based IoT data. In addition, the time to generate IoT data was improved because the cumulative use of transactions processed through similarity of blockchain-based IoT data was managed in a group of certain sizes to link the blocks consistently.

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References

  1. Hong Z, Huang H, Guo S, Chen W, Zheng Z (2019) QOS-aware cooperative computation of?oading for robot swarms in cloud robotics. IEEE Transactions on Vehicular Technology 68(4):4027–4041

    Article  Google Scholar 

  2. Ahmed E, Ahmed A, Yaqoob I, Shuja J, Gani A, Imran M, Shoaib M (2017) Bring computation closer toward the user network: Is edge computing the solution?. IEEE Communication Magazine 55 (11):138–144

    Article  Google Scholar 

  3. Kang J, Yu R, Huang X, Wu M, Maharjan S, Xie S, Zhang Y (2019) Blockchain for secure and efficient data sharing in vehicular edge computing and networks. IEEE Internet of Things Journal 6(3):4660–4670

    Article  Google Scholar 

  4. Shi W, Cao J, Zhang Q, Li Y, Xu L (2016) Edge computing: Vision and challenges. IEEE Internet of Things Journal 3(5):637–646

    Article  Google Scholar 

  5. Miller D (2018) Blockchain and the Internet of Things in the industrial sector. IT Professional 20(3):15–18

    Article  Google Scholar 

  6. Liang X, Zhao J, Shetty S, Li D (2017) Towards data assurance and resilience in IoT using blockchain. Proceeding of the IEEE Military Communications Conference 1:261–266

    Google Scholar 

  7. Aitzhan NZ, Svetinovic D (2018) Security and privacy in decentralized energy trading through multi-signatures, blockchain and anonymous messaging streamsm. IEEE Transactions on Dependable Secure Computing 15(5):840–852

    Article  Google Scholar 

  8. Teslya N, Ryabchikov I (2017) Blockchain-based platform architecture for industrial IoT. Proceedings of the 21st Conference of Open Innovations Association 1:321–329

    Google Scholar 

  9. Xu X, Weber I, Staples M, Zhu L, Bosch J, Bass L, Pautasso C, Rimba A (2017) A taxonomy of blockchain-based systems for architecture design. Proceedings of the IEEE International Conference on Software Architecture (ICSA17) 1:243–252

    Google Scholar 

  10. ETSI (2013) Network functions virtualisation (NFV)-White Paper, Issue 1, 1-20, ETSI Portal

  11. ETSI (2014) Mobile-edge computing-introductory Technical-White Paper, Issue 1, 1-36, ETSI Portal

  12. Ouaddah A, Elkalam AA, Ouahman AA (2017) Towards a novel privacy-preserving access control model based on blockchain technology in IoT, Proceedings of the Europe and MENA Cooperation Advances in Information and Communication Technologies, 523–533

  13. Andrew M, Yu X, Kyle C, Elaine S, Dawn S (2016) The Honey badger of BFT protocols, Proceedings of the 2016 ACM SIGSAC Conference on Computer and Communications Security, 31–42

  14. Rafael P, Elaine S (2017) FruitChains: a fair blockchain, Proceedings of the ACM Symposium on Principles of Distributed Computing, 315–324

  15. Hary K, Steven G, Xiaoqi C, Mathew WS, Edward WF (2018) Arbitrum : scalable, private smart contracts, Proceedings of the 27th USENIX Conference on Security Symposium, 1353–1370

  16. Satyanarayanan M, Bahl P, Caceres R, Davies N (2009) The case for VM-based cloudlets in mobile computing. IEEE Pervasive Computing 8(4):14–23

    Article  Google Scholar 

  17. Tzanakaki A, Anastasopoulos MP, Zervas GS, Rofoee BR, Nejabati R, Simeonidou D (2013) Virtualization of heterogeneous wireless optical network and IT infrastructures in support of cloud and mobile cloud services. IEEE Communications Magazine 51(8):155–161

    Article  Google Scholar 

  18. Giuseppe A, Randal CB, Reza C, Joseph H, Lea K, Peterson ZNJ, Dawn S (2007) Provable data possession at untrusted stores, Proceedings of the 14th ACM conference on Computer and communications security, 598–609

  19. Liu Q, Dulman S, Warnier M (2013) Area: an automatic runtime evolutionary adaptation mechanism for creating self-adaptation algorithms in wireless networks, Proceedings of the Spatial Computing Workshop in AAMAS, 23–28

  20. Mao H, Alizadeh M, Menache I, Kandula S (2016) Resource management with deep reinforcement learning, Proceedings of the 15th ACM Workshop on Hot Topics in Networks, 50– 56

  21. Ye H, Li GY (2018) Deep reinforcement learning for resource allocation in v2v communications, Proceedings of the IEEE International Conference on Communications (ICC), 1–6

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Authors and Affiliations

  1. Department of Information and Communication Convergence Engineering, Mokwon University, 88, Doanbuk-ro, Seo-gu, Daejeon, 35349, South Korea

    Yoon-Su Jeong

  2. Division of Software Liberal Arts, Mokwon University, 88, Doanbuk-ro, Seo-gu, Daejeon, 35349, South Korea

    Yong-Ho Yon

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  1. Yoon-Su Jeong
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Correspondence to Yong-Ho Yon.

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Jeong, YS., Yon, YH. A blockchain-based IoT data management scheme using Bernoulli distribution convergence in the mobile edge computing. Pers Ubiquit Comput 27, 1077–1086 (2023). https://doi.org/10.1007/s00779-020-01459-3

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  • DOI: https://doi.org/10.1007/s00779-020-01459-3

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