Skip to main content
Log in

Edge mining resources allocation among normal and gap blockchains using game theory

  • Published:
The Journal of Supercomputing Aims and scope Submit manuscript

Abstract

Blockchain is a promising technology that may act as a distributed ledger for Internet of Things (IoT). To maintain blockchain and save operation cost, IoT nodes may play roles of miners (who generate or mine blocks), by renting mining rigs from edge computing service providers. During the mining process, miners will receive rewards, while these rewards incentivize miners to keep mining in turn. However, if the rewards are insufficient, there might be a gap time where no miners participate in mining. We call a blockchain without a gap time a normal chain and a gap chain otherwise. This paper is the first to investigate the allocation of edge computing resources when normal and gap chains coexist. In this paper, we propose a normal gap game to model the allocation over the two types of blockchains which was never proposed before. In our game, miners compete with each other to maximize their respective utilities, by determining the per chain shares and the starting time of their mining rigs. We then develop a calculation framework, which factors in various system parameters to quantify the investment and allocation of mining rigs. We finally evaluate the miners’ utilities under different scenarios. This study is very helpful to assist miners for reasonable investment and allocation of edge computing resources.

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

Access this article

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

Instant access to the full article PDF.

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

Similar content being viewed by others

References

  1. Nakamoto S (2008) Decentralized Business Review p. 21260

  2. Wood G et al (2014) Ethereum: a secure decentralised generalised transaction ledger. Ethereum Proj Yellow Pap 151(2014):1

    Google Scholar 

  3. Xiong Z, Feng S, Wang W, Niyato D, Wang P, Han Z (2018) IEEE Internet of Things Journal 6(3), 4585. Publisher: IEEE

  4. Chen Y, Li Z, Yang B, Nai K, Li K (2020) Future Generation Computer Systems. Elsevier, Netherlands

    Google Scholar 

  5. Wu Y, Chen X, Shi J, Ni K, Qian L, Huang L, Zhang K (2018) Sensors 18(10), 3472. Publisher: Multidisciplinary Digital Publishing Institute

  6. Fan Y, Shen G, Jin Z, Hu D, Shi L, Yuan X (2020) in Proceedings of the ACM Turing Celebration Conference-China, pp. 225–229

  7. Tsabary I, Eyal I (2018) in Proceedings of the 2018 ACM SIGSAC Conference on Computer and Communications Security, pp. 713–728

  8. Jiang S, Wu J (2019) in 2019 IEEE International Conference on Blockchain (Blockchain) (IEEE), pp. 107–115

  9. Liu Y, Ke J, Xu Q, Jiang H, Wang H (2019) Decentralization is vulnerable under the gap game. IEEE Access 7:90999

    Article  Google Scholar 

  10. Kiayias A, Koutsoupias E, Kyropoulou M, Tselekounis Y (2016) in Proceedings of the 2016 ACM Conference on Economics and Computation, pp. 365–382

  11. Wang W, Hoang DT, Hu P, Xiong Z, Niyato D, Wang P, Wen Y, Kim DI (2019) IEEE Access 7, 22328. Publisher: IEEE

  12. Wei Y, Xiao M, Yang N, Leng S (2020) IEEE Access 8, 134800. Publisher: IEEE

  13. Chiu J, Koeppl TV (2017) Available at SSRN 3048124

  14. Li W, Cao M, Wang Y, Tang C, Lin F (2020) IEEE Access 8, 101049. Publisher: IEEE

  15. Liu Y, Ke J, Xu Q, Jiang H, Wang H (2019) IEEE Access. IEEE, USA

  16. Di L, Yuan GX, Zeng T (2021) The European Journal of Finance 27(4-5), 419. Publisher: Taylor & Francis

  17. Gong T, Minaei M, Sun W, Kate A (2020) arXiv preprint arXiv:2007.11480

  18. Arenas M, Reutter J, Toussaint E, Ugarte M, Vial F, Vrgoč D (2020) in 37th International Symposium on Theoretical Aspects of Computer Science (STACS 2020) (Schloss Dagstuhl-Leibniz-Zentrum für Informatik)

  19. Xiong Z, Zhang Y, Niyato D, Wang P, Han Z (2018) IEEE Communications Magazine 56(8), 33. Publisher: IEEE

  20. Guo S, Dai Y, Guo S, Qiu X, Qi F (2020) IEEE Transactions on Vehicular Technology 69(5), 5549. Publisher: IEEE

Download references

Acknowledgements

This work is funded in part by the National Natural Science Foundation of China (File no. 61872451 and 61872452), in part by the Science and Technology Development Fund, Macau SAR (File no. 0098/2018/A3, 0037/2020/A1 and 0062/2020/A2) and in part by Xiong’An Independently Controllable Blockchain Infrastructure Project (2020). Qinglin Zhao is the corresponding author.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Qinglin Zhao.

Additional information

Publisher's Note

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

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Yuan, J., Zhao, Q., Li, J. et al. Edge mining resources allocation among normal and gap blockchains using game theory. J Supercomput 78, 9934–9951 (2022). https://doi.org/10.1007/s11227-021-04249-7

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11227-021-04249-7

Keywords

Navigation