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Blockchain-Enabled Intelligent IoT

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Intelligent Internet of Things Networks

Part of the book series: Wireless Networks ((WN))

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Abstract

The past few years have witnessed an exponential growth of diverse Internet of Things (IoT) devices as well as compelling applications ranging from industrial production, intelligent transport, and warehouse logistics to medical care. Dramatic advances in IoT technology not only bring enormous economic opportunities but also challenges. Recently, with the appearance of blockchain technology, the integration of IoT and blockchain (BCoT) is considered a promising solution to address these issues. Blockchain provides a secure and scalable data management framework for IoT devices. However, the huge computation and energy cost of the consensus process in blockchain prevents it from being directly applied as a generic platform. To overcome this challenge, we first propose a cloud mining pool-aided BCoT architecture. Based on this architecture, we study the mining pool selection problem and analyze the colony behaviors of IoT devices with different pooling strategies. We propose a centralized evolutionary game-based pool selection algorithm for the sake of maximizing the system utility. Secondly, to overcome the power and computation constraints of the IoT devices in the blockchain platform, we introduce the cloud computing service to the blockchain platform for the sake of assisting to offload computational task from the IIoT network itself. Also, we study the resource management and pricing problem between the cloud provider and miners. And a multi-agent reinforcement learning algorithm is conceived for searching the near-optimal policy.

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References

  1. T. Mai, H. Yao, N. Zhang, L. Xu, M. Guizani, S. Guo, Cloud mining pool aided blockchain-enabled internet of things: an evolutionary game approach. IEEE Trans. Cloud Comput. 11(1) 692–703 (2023). https://doi.org/10.1109/TCC.2021.3110965

    Article  Google Scholar 

  2. H. Yao, T. Mai, J. Wang, Z. Ji, C. Jiang, Y. Qian, Resource trading in blockchain-based industrial internet of things. IEEE Trans. Ind. Inf. 15(6), 3602–3609 (2019)

    Article  Google Scholar 

  3. X. Huang, R. Yu, J. Kang, Z. Xia, Y. Zhang, Software defined networking for energy harvesting internet of things. IEEE Internet Things J. 5(3), 1389–1399 (2018)

    Article  Google Scholar 

  4. H. Dai, Z. Zheng, Y. Zhang, Blockchain for internet of things: a survey. IEEE Int. Things J. 6(5), 8076–8094 (2019)

    Article  Google Scholar 

  5. Z. Zheng, S. Xie, H. Dai, X. Chen, H. Wang, Blockchain challenges and opportunities: a survey. Int. J. Web Grid Serv. 14(4), 352–375 (2018)

    Article  Google Scholar 

  6. Z. Li, J. Kang, R. Yu, D. Ye, Q. Deng, Y. Zhang, Consortium blockchain for secure energy trading in industrial internet of things. IEEE Trans. Ind. Inf. 14(8), 3690–3700 (2018)

    Google Scholar 

  7. I. Bentov, A. Gabizon, A. Mizrahi, Cryptocurrencies without proof of work, in Financial Cryptography and Data Security: FC 2016 International Workshops, BITCOIN, VOTING, and WAHC, Christ Church, Barbados, February 26, 2016, Revised Selected Papers 20 (Springer, Berlin, 2016), pp. 142–157

    Book  Google Scholar 

  8. C. Qiu, H. Yao, X. Wang, N. Zhang, F.R. Yu, D. Niyato, AI-Chain: blockchain energized edge intelligence for beyond 5G networks. IEEE Netw. 34(6), 62–69 (2020)

    Article  Google Scholar 

  9. C. Qiu, H. Yao, C. Jiang, S. Guo, F. Xu, Cloud computing assisted blockchain-enabled internet of things. IEEE Trans. Cloud Comput. 10(1), 247–257 (2022). https://doi.org/10.1109/TCC.2019.2930259

    Article  Google Scholar 

  10. X. Xu, H. Zhao, H. Yao, S. Wang, A blockchain-enabled energy-efficient data collection system for UAV-assisted IoT. IEEE Internet Things J. 8(4), 2431–2443 (2020)

    Article  Google Scholar 

  11. C. Esposito, A. De Santis, G. Tortora, H. Chang, K.R. Choo, Blockchain: a panacea for healthcare cloud-based data security and privacy?. IEEE Cloud Comput. 5(1), 31–37 (2018)

    Article  Google Scholar 

  12. P. Yang, N. Zhang, Y. Bi, L. Yu, X.S. Shen, Catalyzing cloud-fog interoperation in 5G wireless networks: an SDN approach. IEEE Netw. 31(5), 14–20 (2017)

    Article  Google Scholar 

  13. Z. Xiong, S. Feng, W. Wang, D. Niyato, P. Wang, Z. Han, Cloud/fog computing resource management and pricing for blockchain networks. IEEE Internet Things J. 6(3), 4585–4600 (2019)

    Article  Google Scholar 

  14. X. Liu, W. Wang, D. Niyato, N. Zhao, P. Wang, Evolutionary game for mining pool selection in blockchain networks. IEEE Wireless Commun. Lett. 7(5), 760–763 (2018)

    Article  Google Scholar 

  15. N. Houy, The bitcoin mining game. Available at SSRN 2407834 (2014)

    Google Scholar 

  16. A. Kiayias, E. Koutsoupias, M. Kyropoulou, Y. Tselekounis, Blockchain Mining Games (2016), pp. 365–382

    Google Scholar 

  17. Y. Liu, C. Yang, L. Jiang, S. Xie, Y. Zhang, Intelligent edge computing for IoT-based energy management in smart cities. IEEE Netw. 33(2), 111–117 (2019)

    Article  Google Scholar 

  18. J. Hofbauer, K. Sigmund, Evolutionary game dynamics. Bull. Am. Math. Soc. 40(4), 479–519 (2011)

    Article  MathSciNet  MATH  Google Scholar 

  19. Z. Liu, N.C. Luong, W. Wang, D. Niyato, P. Wang, Y. Liang, D.I. Kim, A survey on blockchain: a game theoretical perspective. IEEE Access 7, 47 615–47 643 (2019)

    Google Scholar 

  20. C. Taylor, D. Fudenberg, A. Sasaki, M.A. Nowak, Evolutionary game dynamics in finite populations. Bull. Math. Biol. 66(6), 1621–1644 (2004)

    Article  MathSciNet  MATH  Google Scholar 

  21. D. Friedman, Evolutionary games in economics. Econometrica 59(3), 637–666 (1991)

    Article  MathSciNet  MATH  Google Scholar 

  22. R. Cressman, C. Ansell, K. Binmore, Evolutionary Dynamics and Extensive Form Games, vol. 5 (MIT Press, Cambridge, 2003)

    Book  Google Scholar 

  23. F. Mazenc, S. Niculescu, Lyapunov stability analysis for nonlinear delay systems. Syst. Control Lett. 42(4), 245–251 (2001)

    Article  MathSciNet  MATH  Google Scholar 

  24. T. Mekki, I. Jabri, A. Rachedi, M.B. Jemaa, Vehicular cloud networking: evolutionary game with reinforcement learning based access approach. Int. J. Bio-inspir. Comput. 13(1), 45–58 (2019)

    Article  Google Scholar 

  25. W. He, Y. Liu, H. Yao, T. Mai, N. Zhang, F.R. Yu, Distributed variational Bayes-based in-network security for the internet of things. IEEE Trans. Cloud Comput. 8(8), 6293–6304 (2021). https://doi.org/10.1109/JIOT.2020.3041656

    Google Scholar 

  26. L. Busoniu, R. Babuska, B. De Schutter, Multi-agent reinforcement learning: a survey, in 2006 9th International Conference on Control, Automation, Robotics and Vision (IEEE, 2006), pp. 1–6

    Google Scholar 

  27. M.T.J. Spaan, Partially observable Markov decision processes, in Reinforcement Learning: State-of-the-Art (Springer, Berlin, 2012), pp. 387—414

    Google Scholar 

  28. X. Yuan, H. Yao, J. Wang, T. Mai, M. Guizani, Artificial intelligence empowered QoS-oriented network association for next-generation mobile networks. IEEE Trans. Cogn. Commun. Netw. 7(3), 856–870 (2021). https://doi.org/10.1109/TCCN.2021.3065463

    Article  Google Scholar 

  29. J. Wang, C. Jiang, H. Zhang, Y. Ren, K.-C. Chen, L. Hanzo, Thirty years of machine learning: the road to pareto-optimal wireless networks. IEEE Commun. Surv. Tutorials 22(3), 1472–1514 (2020). https://doi.org/10.1109/COMST.2020.2965856

    Article  Google Scholar 

  30. P. Hernandezleal, B. Kartal, M.E. Taylor, A survey and critique of multiagent deep reinforcement learning. Auton. Agent. Multi-Agent Syst. 33(6), 750–797 (2019)

    Article  Google Scholar 

  31. J. Wang, C. Jiang, K. Zhang, X. Hou, Y. Ren, Y. Qian, Distributed Q-learning aided heterogeneous network association for energy-efficient IIot. IEEE Trans. Ind. Inf. (2019). https://doi.org/10.1109/TII.2019.2954334

  32. D. Bloembergen, K. Tuyls, D. Hennes, M. Kaisers, Evolutionary dynamics of multi-agent learning: a survey. J. Artif. Intell. Res. 53(1), 659–697 (2015)

    Article  MathSciNet  MATH  Google Scholar 

  33. Y. Zhang, R. Yu, M. Nekovee, Y. Liu, S. Xie, S. Gjessing, Cognitive machine-to-machine communications: visions and potentials for the smart grid. IEEE Netw. 26(3), 6–13 (2012)

    Article  Google Scholar 

  34. D. Niyato, E. Hossain, Dynamics of network selection in heterogeneous wireless networks: an evolutionary game approach. IEEE Trans. Veh. Technol. 58(4), 2008–2017 (2009)

    Article  Google Scholar 

  35. D. Silver, G. Lever, N. Heess, T. Degris, D. Wierstra, M. Riedmiller, Deterministic policy gradient algorithms, in International Conference on Machine Learning, PMLR (2014), pp. 387–395

    Google Scholar 

  36. Y. Zhang, R. Yu, M. Nekovee, Y. Liu, Cognitive machine-to-machine communications: visions and potentials for the smart grid. Netw. IEEE 26(3), 6–13 (2012)

    Article  Google Scholar 

  37. C. Qiu, X. Wang, H. Yao, J. Du, F.R. Yu, S. Guo, Networking integrated cloud–edge–end in IoT: a blockchain-assisted collective q-learning approach. IEEE Internet Things J. 8(16), 12 694–12 704 (2020)

    Google Scholar 

  38. T.T.A. Dinh, R. Liu, M. Zhang, G. Chen, B.C. Ooi, J. Wang, Untangling blockchain: a data processing view of blockchain systems. IEEE Trans. Knowl. Data Eng. 30(7), 1366–1385 (2018)

    Article  Google Scholar 

  39. J. Kang, R. Yu, X. Huang, S. Maharjan, Y. Zhang, E. Hossain, Enabling localized peer-to-peer electricity trading among plug-in hybrid electric vehicles using consortium blockchains. IEEE Trans. Ind. Inf. 13(6), 3154–3164 (2017)

    Article  Google Scholar 

  40. H. Yang, J. Yuan, H. Yao, Q. Yao, A. Yu, J. Zhang, Blockchain-based hierarchical trust networking for jointcloud. IEEE Internet Things J. 7(3), 1667–1677 (2019)

    Article  Google Scholar 

  41. N. Teslya, I. Ryabchikov, Blockchain-based platform architecture for industrial IoT, in The 21st Conference of Open Innovations Association (FRUCT), Helsinki (2017), pp. 321–329

    Google Scholar 

  42. S. Nakamoto, Bitcoin: a peer-to-peer electronic cash system. Working Papers (2008)

    Google Scholar 

  43. B. Laurie, R. Clayton, Proof-of-work proves not to work, in Workshop on Economics and Information, Security (2004)

    Google Scholar 

  44. A. Gervais, G.O. Karame, K. Wüst, V. Glykantzis, H. Ritzdorf, S. Capkun, On the security and performance of proof of work blockchains, in ACM SIGSAC Conference on Computer and Communications Security, Vienna (2016), pp. 3–16

    Google Scholar 

  45. D. Niyato, E. Hossain, Competitive pricing for spectrum sharing in cognitive radio networks: dynamic game, inefficiency of NASH equilibrium, and collusion. IEEE J. Sel. Areas Commun. 26(1), 192–202 (2008)

    Article  Google Scholar 

  46. L. Busoniu, R. Babuska, B. De Schutter, A comprehensive survey of multiagent reinforcement learning. IEEE Trans. Syst. Man Cybern. 38(2), 156–172 (2008)

    Article  Google Scholar 

  47. Y. Zhang, R. Yu, S. Xie, W. Yao, Home M2M networks: architectures, standards, and QoS improvement. IEEE Commun. Mag. 49(4), 44–52 (2011)

    Article  Google Scholar 

  48. L. Xiao, Y. Li, J. Liu, Y. Zhao, Power control with reinforcement learning in cooperative cognitive radio networks against jamming. J. Supercomput. 71(9), 3237–3257 (2015)

    Article  Google Scholar 

  49. M. Bowling, M. Veloso, Multiagent learning using a variable learning rate. Artif. Intel. 136(2), 215–250 (2002)

    Article  MathSciNet  MATH  Google Scholar 

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Yao, H., Guizani, M. (2023). Blockchain-Enabled Intelligent IoT. In: Intelligent Internet of Things Networks . Wireless Networks. Springer, Cham. https://doi.org/10.1007/978-3-031-26987-5_7

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  • DOI: https://doi.org/10.1007/978-3-031-26987-5_7

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  • Publisher Name: Springer, Cham

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