Quantitative cooperation analysis among cross-chain smart contracts | Neural Computing and Applications
Skip to main content

Advertisement

Springer Nature Link
Log in

Quantitative cooperation analysis among cross-chain smart contracts

  • Original Article
  • Published:
Neural Computing and Applications Aims and scope Submit manuscript

Abstract

In cross-chain scenarios, there are different blockchains that need cooperation. The cooperation between different blockchains is completed through smart contracts, which jointly complete cross-chain tasks. When numerous cooperative smart contracts are involved, smart contracts form a complex interactive network, which makes it difficult to evaluate the cooperation. A general model is needed to quantitatively analyze the cross-chain cooperation of associated smart contracts. In this paper, we model the cooperation among smart contracts as conditions and their corresponding actions, the quantitative condition-trigger model. Then, a method of calculating trigger probability by using graph weight is proposed. As the edge weight lacks the information of interaction probability, we introduce the dimension of the edge weight to calculate the interaction probability. The results show that the proposed method can effectively analyze the cross-chain cooperation between smart contracts.

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

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
¥17,985 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price includes VAT (Japan)

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
Fig. 14
Fig. 15
Fig. 16
Fig. 17
Fig. 18
Fig. 19
Fig. 20

Similar content being viewed by others

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

Availability of data and material

Not applicable

Code availability

Not applicable

Notes

  1. https://www.blockchain.com/charts/mempool-count.

References

  1. Kan L, Wei Y, Muhammad A H, et al (2018) A multiple blockchains architecture on inter-blockchain communication. In: 2018 IEEE international conference on software quality, reliability and security companion (QRS-C). IEEE, pp 139–145

  2. Wang H, Cen Y, Li X (2017) Blockchain router: a cross-chain communication protocol[AC]. In: Proceedings of the 6th international conference on informatics, environment, energy and applications. ACM, pp 94–97

  3. Li W, Sforzin A, Fedorov S, Karame G (2017) Towards scalable and private industrial blockchains. In: Proceedings of the ACM workshop on blockchain, cryptocurrencies and contracts, pp 9–14

  4. Gaži P, Kiayias A, Zindros D (2019) Proof-of-stake sidechains. In: 2019 IEEE symposium on security and privacy (SP). IEEE, pp 139–156

  5. Dang H, Dinh TTA, Loghin D, et al (2019) Towards scaling blockchain systems via sharding. Proceedings of the 2019 international conference on management of data, pp 123–140

  6. Kannengießer N, Pfister M, Greulich M, et al (2020) Bridges between islands: cross-chain technology for distributed ledger technology. Proceedings of the 53rd hawaii international conference on system sciences

  7. Borkowski M (2019) Cross blockchain technologies: review, state of the art, and outlook. http://dsg.tuwien.ac.at/projects/tast/pub/tast-white-paper-4. pdf. White Paper, Technische Universität Wien. Version, 1

  8. Zie JY, Deneuville JC, Briffaut J et al (2019) Extending atomic cross-chain swaps. In: Data privacy management, Cryptocurrencies and Blockchain Technology. Springer, Cham, pp 219–229

  9. Liu Z, Xiang Y, Shi J et al (2019) Hyperservice: Interoperability and programmability across heterogeneous blockchains. In: Proceedings of the 2019 ACM SIGSAC conference on computer and communications security, pp 549–566

  10. Dolgui A, Ivanov D, Potryasaev S et al (2020) Blockchain-oriented dynamic modelling of smart contract design and execution in the supply chain. Int J Prod Res 58(7):2184–2199

    Article  Google Scholar 

  11. Li T et al (2021) FAPS: a fair, autonomous and privacy-preserving scheme for big data exchange based on oblivious transfer, Ether cheque and smart contracts. Inf Sci 12(544):469–84

    Article  MathSciNet  Google Scholar 

  12. Dwivedi AD, et al. Optimized blockchain model for internet of things based healthcare applications. In: 2019 42nd international conference on telecommunications and signal processing (TSP) 2019 Jul 1 (pp 135–139). IEEE

  13. Dai B, Jiang S, Zhu M, et al. (2020) Research and implementation of cross-chain transaction model based on improved hash-locking. In: International conference on blockchain and trustworthy systems. Springer, Singapore, pp 218–230

  14. Herlihy M (2018) Atomic cross-chain swaps. In: Proceedings of the 2018 ACM symposium on principles of distributed computing. ACM, pp 245–254

  15. Wang Q, Wang S, Zhang P et al (2019) An achieving data exchange cross-chain alliance protocol. J Phys Conf Ser 1213:042037

    Article  Google Scholar 

  16. Lin W, Yin X, Wang S, et al (2020) A Blockchain-enabled decentralized settlement model for IoT data exchange services. Wireless Netw, 1–15

  17. Van Glabbeek R , Gramoli V , Tholoniat P (2019) Cross-chain payment protocols with success guarantees

  18. Chen YH, Chen SH, Lin IC (2018) Blockchain based smart contract for bidding system. In: 2018 IEEE international conference on applied system invention (ICASI). IEEE, pp 208–211

  19. Mengelkamp E, Notheisen B, Beer C et al (2018) A blockchain-based smart grid: towards sustainable local energy markets. Comput Sci Res Develop 33(1–2):207–214

    Article  Google Scholar 

  20. Karumba S, Kanhere SS, Jurdak R, Sethuvenkatraman S (2020) HARB: a hypergraph-based adaptive consortium blockchain for decentralised energy trading. IEEE Internet Things J. https://doi.org/10.1109/JIOT.2020.3022045

    Article  Google Scholar 

  21. Zhang B, Jiang C, Yu JL et al (2016) A contract game for direct energy trading in smart grid. IEEE Trans Smart Grid 9(4):2873–2884

    Article  Google Scholar 

  22. Qasse Ilham A, Talib Manar Abu, Nasir Qassim (2019) Inter blockchain communication: a survey. In: Proceedings of the ArabWIC 6th annual international conference research track (ArabWIC 2019). Association for Computing Machinery, New York, NY, USA, Article 2, pp 1–6

  23. Kakimoto M, Endoh Y, Shin H et al (2018) Probabilistic solar irradiance forecasting by conditioning joint probability method and its application to electric power trading. IEEE Trans Sustain Energy 10(2):983–993

    Article  Google Scholar 

  24. Azaïs R, Bardet JB, Génadot A et al (2014) Piecewise deterministic Markov process-recent results. In: ESAIM: Proceedings. EDP Sciences 44:276–290

Download references

Funding

This work was supported in part by the National Natural Science Foundation of China under Grant No. 61772352; National Key Research and Development Project under Grant No. 2020YFB1711800 and 2020YFB1707900; the Science and Technology Project of Sichuan Province under Grant No. 2019YFG0400, 2020YFG0479, 2020YFG0322, and the R&D Project of Chengdu City under Grant No. 2019-YF05-01790-GX.

Author information

Authors and Affiliations

  1. Sichuan University, No. 24 South Section 1, Yihuan Road, Chengdu, Sichuan, China

    Hong Su, Bing Guo, Junyu Lu & Xinhua Suo

  2. Geely University of China, Chengdu, Sichuan, China

    Hong Su

Authors
  1. Hong Su
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Bing Guo
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Junyu Lu
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Xinhua Suo
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Hong Su.

Ethics declarations

Conflicts of interest/Competing interests

The authors declare that they have no conflict of interest.

Ethics approval

Not applicable

Consent to participate

Not applicable

Consent for publication

Not applicable

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

Su, H., Guo, B., Lu, J. et al. Quantitative cooperation analysis among cross-chain smart contracts. Neural Comput & Applic 34, 9847–9862 (2022). https://doi.org/10.1007/s00521-022-06970-7

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00521-022-06970-7

Keywords

Search

Navigation