Abstract
With the continuous development of the Internet of Things, more and more social sectors and smart devices are connected to the Internet of Things. This has led to a spurt of data growth and posed challenges to data security. To solve this problem, the Internet of Things needs a more secure and efficient storage method. Nowadays, one of the most important creative technological advancements that plays a significant role in the professional world is blockchain technology. And Both academia and industry attach great importance to the research of blockchain application technology. Some scholars believe that the blockchain itself is a secure distributed database. So, Blockchain is also considered a safe way to store data. In this paper, we introduce blockchain into the Internet of Things to ensure the security of Internet of Things data. At the same time, we have solved the problem of quantifying the degree of blockchain decentralization, which provides conditions for system optimization. After that, we proposed a system optimization model based on deep reinforcement learning to dynamically adjust system parameters. The simulation results show that the decentralization of the blockchain and the security of the system are guaranteed, and the throughput of the system has been improved.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Vijayalakshmi, S.R., Muruganand S.: A survey of internet of things in fire detection and fire industries. In: Proceedings of the I-SMAC, Coimbatore, India, pp. 703–707 (2017)
Shynu, P.G., Menno, V.G., Kumar, R.L., Kadry, S., Nam, Y.: Blockchain-based secure healthcare application for diabetic-cardio disease prediction in fog computing. IEEE Access 9, 45706–45720 (2021)
Cocco, L., Mannaro, K., Tonelli, R., Mariani, L., Lodi, M.B.: A blockchain-based trace- ability system in agri-food SME: case study of a traditional bakery. IEEE Access 9, 62899–62915 (2021)
Hu, S., Huang, S., Huang, J., Su, J.F.: Blockchain and edge computing technology enabling organic agricultural supply chain: a framework solution to trust crisis. Comput. Ind. Eng. 153, 107079 (2021)
Zhang, Y., Wang, Z., Deng, J., Gong, Z., Flood, I., Wang, Y.: Framework for a blockchain-based infrastructure project financing system. IEEE Access 9, 141555–141570 (2021)
Barenji, A.V., Guo, H., Tian, Z.G., Li, Z., Wang, W.M., et al.: Blockchain-based cloud manufacturing: decentralization. arXiv:1901.10403 (2019)
Jogunola, O., Adebisi, B., Ikpehai, A., Popoola, S.I., Gui, G.: Consensus algorithms and deep reinforcement learning in energy market: a review. IEEE Internet Things 8(6), 4211–4227 (2021)
Yang, L., Zhang, H., Zhu, X., Sheng, X.: Ball motion control in the table tennis robot system using time-series deep reinforcement learning. IEEE Access 9, 99816–99827 (2021)
Li, Y., Han, W., Wang, Y.: Deep reinforcement learning with application to air confrontation intelligent decision-making of manned/unmanned aerial vehicle cooperative system. IEEE Access 8, 67887–67898 (2020)
Luis, S.Y., Reina, D.G., Marín, S.L.T.: A multiagent deep reinforcement learning approach for path planning in autonomous surface vehicles: the Ypacaraí lake patrolling case. IEEE Access 9, 17084–17099 (2021)
Li, Y., Ni, P., Chang, V.: Application of deep reinforcement learning in stock trading strategies and stock forecasting. Computing 102(6), 1305–1322 (2019). https://doi.org/10.1007/s00607-019-00773-w
Sivakumar, N.R., Ibrahim, A.Z.: Deep neural artificial intelligence for IoT based tele health data analytics. Comput. Mater. Continua 70(3), 4467–4483 (2022)
Nian, R., Liu, J.F., Huang, B.: A review on reinforcement learning: Introduction and applications in industrial process control. Comput. Chem. Eng., 139, 106886 (2020)
Hatem, M., Foudil, A.: Simulation of the navigation of a mobile robot by the Q-Learning using artificial neuron networks. In: Proceedings of the ICCA, Saida, Algeria (2009)
Tu, S., et al.: Reinforcement learning assisted impersonation attack detection in device-to-device communications. IEEE Trans. Veh. Technol. 70(2), 1474–1479 (2021)
Wan, J., Waqas, M., Tu, S., Hussain, S.M., Shah, A.: An efficient impersonation attack detection method in fog computing. Comput. Mater. Continua 68(1), 267–281 (2021)
Zhang, B., Waqas, M., Tu, S., Hussain, S.M., Rehman, S.U.: Power allocation strategy for secret key generation method in wireless communications. Comput. Mater. Continua 2, 2179–2188 (2021)
Karim, O.A., Javaid, N., Sher, A., Wadud, Z., Ahmed, S.: QL-EEBDG: QLearning based energy balanced routing in underwater sensor networks. EAI Endorsed Trans. Energy Web Inf. Technol. 5(17), e15 (2018)
Giannopoulos, A., Spantideas, S., Kapsalis, N., Karkazis, P., Trakadas, P.: Deep reinforcement learning for energy-efficient multi-channel transmissions in 5G cognitive HetNets: centralized, decentralized and transfer learning based solutions. IEEE Access 9, 129358–129374 (2021)
Chen, H., Chen, Z., Lin, F., Zhuang, P.: Effective management for blockchain-based agri-food supply chains using deep reinforcement learning. IEEE Access 9, 36008–36018 (2021)
Paeng, B., Park, I.B., Park, J.: Deep Reinforcement learning for minimizing tardiness in parallel machine scheduling with sequence dependent family setups. IEEE Access 9, 101390–101401 (2021)
Wang, L., Han, D., Zhang, M., Wang, D., Zhang, Z.: Deep reinforcement learning-based adaptive handover mechanism for VLC in a hybrid 6G network architecture. IEEE Access 9, 87241–87250 (2021)
Guidi, B., Michienzi, A.: The decentralization of social media through the blockchain technology. In: Proceedings of the 13th ACM Web Science Conference, WebSci 2021, pp. 138–139 (2021)
Kaur, M., Khan, M.Z., Gupta, S., Noorwali, A., Chakraborty, C.: MBCP: performance analysis of large scale mainstream blockchain consensus protocols. IEEE Access 9, 80931–80944 (2021)
Zarrin, J., Wen Phang, H., Babu Saheer, L., Zarrin, B.: Blockchain for decentralization of internet: prospects, trends, and challenges. Clust. Comput. 24(4), 2841–2866 (2021). https://doi.org/10.1007/s10586-021-03301-8
Sukhwani, H., Martínez, J.M., Chang, X., Trivedi, K.S., Rindos, A.: Performance modeling of PBFT consensus process for permissioned blockchain network (hyperledger fabric). In: Proceedings of the 2017 IEEE 36th Symposium on Reliable Distributed Systems (SRDS), pp. 253–255 (2017)
Hou, L., Xu, X., Zheng, K., Wang, X.: An intelligent transaction migration scheme for raft-based private blockchain in internet of things applications. IEEE Commun. Lett. 25(8), 2753–2757 (2021)
Guerraoui, R., Knezevic, N., Quema, V., Vukolic, M.: The next 700 BFT protocols. In: Proceedings of the 5th European Conference on Computer Systems, pp. 363–376 (2010)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Bai, X., Tu, S., Waqas, M., Wu, A., Zhang, Y., Yang, Y. (2022). Blockchain Enable IoT Using Deep Reinforcement Learning: A Novel Architecture to Ensure Security of Data Sharing and Storage. In: Sun, X., Zhang, X., Xia, Z., Bertino, E. (eds) Artificial Intelligence and Security. ICAIS 2022. Lecture Notes in Computer Science, vol 13340. Springer, Cham. https://doi.org/10.1007/978-3-031-06791-4_46
Download citation
DOI: https://doi.org/10.1007/978-3-031-06791-4_46
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-06790-7
Online ISBN: 978-3-031-06791-4
eBook Packages: Computer ScienceComputer Science (R0)