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Recent Reinforcement Learning and Blockchain Based Security Solutions for Internet of Things: Survey

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Abstract

Users’ security is one of the most important issues in Internet of Things (IoT) due to the high number of IoT devices involved in different applications. Security threats are evolving at a rapid pace that make the current security and privacy measures unsuitable. Therefore, several researchers have been attracted by this domain with the aim of proposing either new or improved solutions to address the problem of security in IoT. Blockchain technology is a relatively new invention in modern IoT applications to solve the security issue. It is based on the use of a public immutable ledger called a blockchain. After conducting a verification process, several parts on a network encode transactions into this ledger. Moreover, Machine learning (ML) algorithms have been used as emerging solutions to improve IoT security. Reinforcement learning (RL) is the most popular machine learning technique proposed to secure IoT systems. Unlike other ML methods, RL can observe, learn and interact with the environment even if it has minimum information about the considered parameters. Various researches have been proposed to treat security problem in IoT based on either RL technique or Blockchain technology or a combination of both techniques. Therefore, we believe there is a need for a comprehensive survey on works proposed in recent years that address security issues using these techniques. In this paper, we provide a summary of research efforts made in the past few years, from 2018 to 2021, addressing security issues using RL and blockchain techniques in the IoT domain.

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References

  1. Gubbi, J., Buyya, R., Marusic, S., & Palaniswami, M. (2013). Internet of Things (IoT): A vision, architectural elements, and future directions. Future Generation Computer Systems, 29(7), 1645–1660.

    Google Scholar 

  2. Al-Fuqaha, A., Guizani, M., Mohammadi, M., Aledhari, M., & Ayyash, M. (2015). Internet of Things: A survey on enabling technologies, protocols, and applications. IEEE Communications Surveys & Tutorials, 17(4), 2347–2376.

    Google Scholar 

  3. Ovidiu Vermesan and Peter Friess. (2013). Internet of Things: Converging technologies for smart environments and integrated ecosystems. River publishers.

    Google Scholar 

  4. Rim, G., Makhlouf, A., & Hamida, S. (2021). Geographical information based clustering algorithm for internet of vehicles. Machine learning for networking (pp. 107–121). Springer.

    Google Scholar 

  5. Rim, G., Makhlouf, A., & Hamida, S. (2020). A weight based clustering algorithm for internet of vehicles. Automatic Control and Computer Sciences, 54(6), 493–500.

    Google Scholar 

  6. Gasmi, R., & Aliouat, M. (2019). Vehicularad hoc networks versus internet of vehicles - a comparative view. In 2019 International Conference on Networking and Advanced Systems(ICNAS), 1–6. IEEE.

  7. Rim, G., Makhlouf, A., & Hamida, S. (2019). A stable link based zone routing protocol (SL-ZRP) for internet of vehicles environment. Wireless Personal Communications, 112(2), 1045–1060.

    Google Scholar 

  8. Tanweer, A. (2018). A reliable communication framework and its use in Internet of Things (IoT). International Journal of Scientific Research in Computer Science, Engineering and Information Technology, 3(5), 450–456.

    Google Scholar 

  9. Singh, S., & Singh, N. (2015). Internet of things (IoT): Security challenges, business opportunities & reference architecture for e-commerce. In 2015 International Conference on Green Computing and Internet of Things (ICGCIoT), 1577–1581. IEEE.

  10. Borgohain, Tuhin., Kumar, Uday., Sanyal, Sugata. (2015). Survey of security and privacy issues of Internet of Things. Retrieved from arXiv preprint arXiv:1501.02211

  11. Roman, R., Zhou, J., & Lopez, J. (2013). On the features and challenges of security and privacy in distributed Internet of Things. Computer Networks, 57(10), 2266–2279.

    Google Scholar 

  12. Kaelbling, L. P., Littman, M. L., & Moore, A. W. (1996). Reinforcement learning: A survey. Journal of artificial intelligence research, 4, 237–285.

    Google Scholar 

  13. Yousefpour, A., Ishigaki, G., Gour, R., & Jue, J. P. (2018). On reducing IoT service delay via fog offloading. IEEE Internet of Things Journal, 5(2), 998–1010.

    Google Scholar 

  14. Ni, J., Zhang, K., Lin, X., & Shen, X. (2017). Securing fog computing for Internet of Things applications: Challenges and solutions. IEEE Communications Surveys & Tutorials, 20(1), 601–628.

    Google Scholar 

  15. Bellendorf, J., & Mann, Z. A. (2020). Classification of optimization problems in fog computing. Future Generation Computer Systems, 107, 158–176.

    Google Scholar 

  16. Zhang, PeiYun, Zhou, MengChu, & Fortino, G. (2018). Security and trust issues in fog computing: A survey. Future Generation Computer Systems, 88, 16–27.

    Google Scholar 

  17. Mukherjee, M., Matam, R., Shu, L., Maglaras, L., Ferrag, M. A., Choudhury, N., & Kumar., V. (2017). Security and privacy in fog computing: Challenges. IEEE Access, 5, 19293–19304.

    Google Scholar 

  18. Hammoudi, S., Aliouat, Z., & Harous, S. (2018). Challenges and research directions for Internet of Things. Telecommunication Systems, 67(2), 367–385.

    Google Scholar 

  19. Botta, A., De Donato, W., Persico, V., & Pescap´e, A. (2016). Integration of cloud computing and Internet of Things: A survey. Future generation computer systems, 56, 684–700.

    Google Scholar 

  20. Mutlag, A. A., Ghani, M. K. A., Arunkumar, N., Mohammed, M. A., Mohd, O., et al. (2019). Enabling technologies for fog computing in healthcare iot systems. Future Generation Computer Systems, 90, 62–78.

    Google Scholar 

  21. Luan, Tom H., Gao, Longxiang., Li, Zhi., Xiang, Yang., Wei, Guiyi., Sun, Limin. (2015). Fog computing: Focusing on mobile users at the edge. Retrieved from arXiv preprint arXiv:1502.01815

  22. Dhingra, S., Madda, R. B., Patan, R., Jiao, P., Barri, K., & Alavi, A. H. (2021). Internet of Things-based fog and cloud computing technology for smart traffic monitoring. Internet of Things, 14, 100175.

    Google Scholar 

  23. Amir Vahid Dastjerdi and Rajkumar Buyya. (2016). Fog computing: Helping the Internet of Things realize its potential. Computer, 49(8), 112–116.

    Google Scholar 

  24. Roman, R., Lopez, J., & Mambo, M. (2018). Mobile edge computing, fog et al.: A survey and analysis of security threats and challenges. Future Generation Computer Systems, 78, 680–698.

    Google Scholar 

  25. Kumar, V., Laghari, A. A., Karim, S., Shakir, M., & Brohi, A. A. (2019). Comparison of fog computing & cloud computing. International Journal of Mathematical Sciences and Computing, 1, 31–41.

    Google Scholar 

  26. Maher Abdelshkour. IoT, from cloud to fog computing. Retreived from https://blogs.cisco.com/perspectives/iot-from-cloud-to-fog-computing

  27. Varghese, Blesson., Wang, Nan., Barbhuiya, Sakil., Kilpatrick, Peter., Nikolopoulos, Dimitrios S. (2016). Challenges and opportunities in edge computing. In 2016 IEEE International Conference on Smart Cloud (SmartCloud), 20–26. IEEE.

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

    Google Scholar 

  29. Stankovic, J. A. (2014). Research directions for the Internet of Things. IEEE Internet of Things Journal, 1(1), 3–9.

    Google Scholar 

  30. Louis Coetzee and Johan Eksteen. The Internet of Things-promise for the future? an introduction. In 2011 IST-Africa Conference Proceedings, pages 1–9. IEEE, 2011.

  31. Lee, In., & Lee, K. (2015). The Internet of Things (IoT): Applications, investments, and challenges for enterprises. Business Horizons, 58(4), 431–440.

    Google Scholar 

  32. Harbi, Y., Aliouat, Z., Harous, S., Bentaleb, A., & Re-foufi, A. (2019). A review of security in Internet of Things. Wireless Personal Communications, 108(1), 325–344.

    Google Scholar 

  33. Alsaadi, E., & Tubaishat, A. (2015). Internet of things: Features, challenges, and vulnerabilities. International Journal of Advanced Computer Science and Information Technology, 4(1), 1–13.

    Google Scholar 

  34. Kai Zhao and Lina Ge. (2013). A survey on the Internet of Things security. In 2013 Ninth international conference on computational intelligence and security, 663–667. IEEE.

  35. Mukrimah Nawir, Amiza Amir, Naimah Yaakob, and Ong Bi Lynn. (2016). Internet of Things (iot): Taxonomy of security attacks. In 2016 3rd International Conference on Electronic Design (ICED), 321–326. IEEE.

  36. Ioannis Andrea, Chrysostomos Chrysostomou, and George Hadjichristofi. (2015). Internet of Things: Security vulnerabilities and challenges. In 2015 IEEE symposium on computers and communication (ISCC), pages 180–187. IEEE.

  37. Mpitziopoulos, A., Gavalas, D., Konstantopoulos, C., & Gram- mati Pantziou. (2009). A survey on jamming attacks and countermeasures in WSNs. IEEE Communications Surveys & Tutorials, 11(4), 42–56.

    Google Scholar 

  38. Jagatic, T. N., Johnson, N. A., Jakobsson, M., & Menczer, F. (2007). Social phishing. Communications of the ACM, 50(10), 94–100.

    Google Scholar 

  39. M Vivekananda Bharathi, Rama Chaithanya Tanguturi, C Jayakumar, and K Selvamani. (2012). Node capture attack in wireless sensor network: A survey. In 2012 IEEE International Conference on Computational Intelligence and Computing Research, 1–3. IEEE.

  40. Shoukat Ali, Muazzam A Khan, Jawad Ahmad, Asad W Malik, and Anis ur Rehman. (2018). Detection and prevention of black hole attacks in IoT & WSN. In 2018 Third International Conference on Fog and Mobile Edge Computing (FMEC), 217–226. IEEE, 2018.

  41. Zhang, K., Liang, X., Rongxing, Lu., & Shen, X. (2014). Sybil attacks and their defenses in the Internet of Things. IEEE Internet of Things Journal, 1(5), 372–383.

    Google Scholar 

  42. Lee, P., Clark, A., Bushnell, L., & Poovendran, R. (2014). A passivity frame-work for modeling and mitigating wormhole attacks on networked control systems. IEEE Transactions on Automatic Control, 59(12), 3224–3237.

    MathSciNet  MATH  Google Scholar 

  43. Benjamin Khoo. (2011). Rfid as an enabler of the Internet of Things: Issues of security and privacy. In 2011 International Conference on Internet of Things and 4th International Conference on Cyber, Physical and Social Computing, 709–712. IEEE.

  44. Sfar, A. R., Natalizio, E., Challal, Y., & Chtourou, Z. (2018). A roadmap for security challenges in the Internet of Things. Digital Communications and Networks, 4(2), 118–137.

    Google Scholar 

  45. Misra, S., Maheswaran, M., & Hashmi, S. (2017). Security challenges and approaches in Internet of Things. Springer.

    Google Scholar 

  46. Md Mahmud Hossain, Maziar Fotouhi, and Ragib Hasan. (2015). Towards an analysis of security issues, challenges, and open problems in the Internet of Things. In 2015 IEEE World Congress on Services, 21–28. IEEE.

  47. Andrey Bogdanov, Miroslav Kneˇzevi´c, Gregor Leander, Deniz Toz, Kerem Varıcı, and Ingrid Verbauwhede. (2011). Spongent: A lightweight hash function. In International workshop on cryptographic hardware and embedded systems, 312–325. Springer.

  48. Aumasson, J.-P., Henzen, L., Meier, W., & Naya-Plasencia, M. (2013). Quark: A lightweight hash. Journal of cryptology, 26(2), 313–339.

    MathSciNet  MATH  Google Scholar 

  49. Miorandi, D., Sicari, S., De Pellegrini, F., & Chlamtac, I. (2012). Internet of Things: Vision, applications and research challenges. Ad hoc networks, 10(7), 1497–1516.

    Google Scholar 

  50. Prabhakaran Kasinathan, Claudio Pastrone, Maurizio A Spirito, and Mark Vinkovits. (2013). Denial-of-service detection in 6lowpan based Internet of Things. In 2013 IEEE 9th international conference on wireless and mobile computing, networking and communications (WiMob), 600–607. IEEE.

  51. Rachel Greenstadt and Jacob Beal. (2008). Cognitive security for personal devices. In Proceedings of the 1st ACM Workshop on Workshop on AISec, 27–30.

  52. Liu, J., Xiao, Y., & Philip Chen, C. L. (2012). Internet of things’ authentication and access control. International Journal of Security and Networks, 7(4), 228–241.

    Google Scholar 

  53. Edewede Oriwoh, Haider al Khateeb, and Marc Conrad. (2016). Responsibility and non-repudiation in resource-constrained Internet of Things scenarios. In International Conference on Computing and Technology Innovation (CTI 2015).

  54. Yasmine, H., Zibouda, A., Allaoua, R., & Harous, S. (2021). Recent security trends in Internet of Things: A comprehensive survey. IEEE Access, 9, 113292–113314.

    Google Scholar 

  55. Flavio Bonomi, Rodolfo Milito, Jiang Zhu, and Sateesh Addepalli. (2012). Fog computing and its role in the Internet of Things. In Proceedings of the first edition of the MCC workshop on Mobile cloud computing, 13–16.

  56. Rongxing, Lu., Heung, K., Lashkari, A. H., & Ghorbani, A. A. (2017). A lightweight privacy-preserving data aggregation scheme for fog computing-enhanced IoT. IEEE Access, 5, 3302–3312.

    Google Scholar 

  57. Alrawais, A., Alhothaily, A., Chunqiang, Hu., Xing, X., & Cheng, X. (2017). An attribute-based encryption scheme to secure fog communications. IEEE access, 5, 9131–9138.

    Google Scholar 

  58. Kwasi, B.-B., Eric, K., Emmanuel, A.-B., & Emmanuel, D. (2019). Encryption protocol for resource-constrained devices in fog-based IoT using one-time pads. IEEE Internet of Things Journal, 2, 3925–3933.

    Google Scholar 

  59. Kalkan, K., & Zeadally, S. (2017). Securing Internet of Things with software defined networking. IEEE Communications Magazine, 56(9), 186–192.

    Google Scholar 

  60. Wang, X., Ke, Xu., Chen, W., Li, Qi., Shen, M., & Bo, Wu. (2020). ID-based SDN for the Internet of Things. IEEE Network, 34(4), 76–83.

    Google Scholar 

  61. Ola Salman, Sarah Abdallah, Imad H Elhajj, Ali Chehab, and Ayman Kayssi. (2016). Identity-based authentication scheme for the internet of things. In 2016 IEEE Symposium on Computers and Communication (ISCC), 1109–1111. IEEE.

  62. Dongdong Ma and Yijie Shi. (2019). A lightweight encryption algorithm for edge networks in software-defined industrial internet of things. In 2019 IEEE 5th International Conference on Computer and Communications (ICCC), pages 1489–1493. IEEE.

  63. Peter Bull, Ron Austin, Evgenii Popov, Mak Sharma, and Richard Watson. (2016). Flow based security for iot devices using an sdn gateway. In 2016 IEEE 4th international conference on future internet of things and cloud (FiCloud), 157–163. IEEE.

  64. Anis Herbadji, Hadjer Goumidi, Yasmine Harbi, Khadidja Medani, and Zibouda Aliouat. (2020). Blockchain for internet of vehicles security. In Blockchain for Cybersecurity and Privacy, pages 159–197. CRC Press.

  65. Axel Moinet, Benoˆıt Darties, and Jean-Luc Baril. ( 2017). Blockchain based trust & authentication for decentralized sensor networks. Retrieved from arXiv preprint arXiv:1706.01730.

  66. Chen, W., Qiu, X., Cai, T., Dai, H.-N., Zheng, Z., & Zhang, Y. (2021). Deep reinforcement learning for internet of things: A comprehensive survey. IEEE Communications Surveys & Tutorials, 23(3), 1659–1692.

    Google Scholar 

  67. Jun Lin, Zhiqi Shen, and Chunyan Miao. (2017). Using blockchain technology to build trust in sharing lorawan IoT. In Proceedings of the 2nd International Conference on Crowd Science and Engineering, 38–43.

  68. Dhanda, S. S., Singh, B., & Jindal, P. (2020). Lightweight cryptography: A solution to secure iot. Wireless Personal Communications, 112(3), 1947–1980.

    Google Scholar 

  69. Peralta, G., Cid-Fuentes, R. G., Bilbao, J., & Crespo, P. M. (2019). Homomorphic encryption and network coding in iot architectures: Advantages and future challenges. Electronics, 8(8), 827.

    Google Scholar 

  70. Varri, U., Pasupuleti, S., & Kadambari, K. V. (2020). A scoping review of searchable encryption schemes in cloud computing: Taxonomy, methods, and recent developments. The Journal of Supercomputing, 76(4), 3013–3042.

    Google Scholar 

  71. Jaweher Zouari, Mohamed Hamdi, and Tai-Hoon Kim. (2017). A privacy-preserving homomorphic encryption scheme for the internet of things. In 2017 13th International Wireless Communications and Mobile Computing Conference (IWCMC), 1939–1944. IEEE.

  72. Hussain, F., Hussain, R., Hassan, S. A., & Hossain, E. (2020). Machine learning in IoT security: Current solutions and future challenges. IEEE Communications Surveys & Tutorials, 22(3), 1686–1721.

    Google Scholar 

  73. Al-Garadi, M. A., Mohamed, A., Al-Ali, A. K., Xiaojiang, Du., Ali, I., & Guizani, M. (2020). A survey of machine and deep learning methods for internet of things (IoT) security. IEEE Communications Surveys & Tutorials, 22(3), 1646–1685.

    Google Scholar 

  74. Dai, H.-N., Zheng, Z., & Zhang, Y. (2019). Blockchain for internet of things: A survey. IEEE Internet of Things Journal, 6(5), 8076–8094.

    Google Scholar 

  75. Ali, M. S., Vecchio, M., Pincheira, M., Dolui, K., Antonelli, F., & Rehmani, M. H. (2018). Applications of blockchains in the internet of things: A comprehensive survey. IEEE Communications Surveys & Tutorials, 21(2), 1676–1717.

    Google Scholar 

  76. Viriyasitavat, W., Anuphaptrirong, T., & Hoonsopon, D. (2019). When blockchain meets internet of things: Characteristics, challenges, and business opportunities. Journal of industrial information integration, 15, 21–28.

    Google Scholar 

  77. Al Sadawi, A., Hassan, M. S., & Ndiaye, M. (2021). A survey on the integration of blockchain with IoT to enhance performance and eliminate challenges. IEEE Access, 9, 54478–54497.

    Google Scholar 

  78. Da Li, Xu., Yang, Lu., & Li, L. (2021). Embedding blockchain technology into IoT for security: A survey. IEEE Internet of Things Journal, 8(13), 10452–10473.

    Google Scholar 

  79. Pohrmen, F. H., Das, R. K., & Saha, G. (2019). Blockchain-based security aspects in heterogeneous internet-of-things networks: A survey. Transactions on Emerging Telecommunications Technologies, 30(10), e3741.

    Google Scholar 

  80. Uprety, A., & Rawat, D. B. (2020). Reinforcement learning for IoT security: A comprehensive survey. IEEE Internet of Things Journal, 8(11), 8693–8706.

    Google Scholar 

  81. Thanh Thi Nguyen and Vijay Janapa Reddi. (2019). Deep reinforcement learning for cyber security. Retreived from arXiv preprint arXiv:1906.05799.

  82. Yulei, Wu., Wang, Z., Ma, Y., & Leung, V. C. M. (2021). Deep reinforcement learning for blockchain in industrial IoT: A survey. Computer Networks, 191, 108004.

    Google Scholar 

  83. Jameel, F., Javaid, U., Khan, W. U., Aman, M. N., Pervaiz, H., & J¨antti, R. (2020). Reinforcement learning in blockchain-enabled iiot networks: A survey of recent advances and open challenges. Sustainability, 12(12), 5161.

    Google Scholar 

  84. S. Nakamoto, “Bitcoin: a peer-to-peer electronic cash system.” 2008, [Online]. Available: https://bitcoin.org/bitcoin.pdf

  85. Zhang, R., Xue, R., & Liu, L. (2019). Security and privacy on blockchain. ACM Computing Surveys (CSUR), 52(3), 1–34.

    Google Scholar 

  86. Lin, C., He, D., Kumar, N., Huang, X., Vijayakumar, P., & Choo, K.-K. (2019). Homechain: A blockchain-based secure mutual authentication system for smart homes. IEEE Internet of Things Journal, 7(2), 818–829.

    Google Scholar 

  87. Atlam, H. F., Alenezi, A., Alassafi, M. O., & Wills, G. (2018). Blockchain with internet of things: Benefits, challenges, and future directions. International Journal of Intelligent Systems and Applications, 10(6), 40–48.

    Google Scholar 

  88. Banafa, A. (2017). IoT and blockchain convergence: benefits and challenges. IEEE Internet of Things, 9.

  89. Zheng, Z., Xie, S., Dai, H., Chen, X., & Wang, H. (2017). An overview of blockchain technology: Architecture, consensus, and future trends. In 2017 IEEE International Congress on Big Data (BigData Congress), (pp.557–564). IEEE.

  90. “ethereum: Blockchain app platforms.”, [online]. Retrieved from https://ethereum.org/en/.

  91. “multichain: Open platform for building blockchains.”, [online]. Retrieved from https://www.multichain.com/.

  92. “hyperledger project”. [online]. Retrieved from https://www.hyperledger.org/.

  93. Ding, S., Cao, J., Li, C., Fan, K., & Li, H. (2019). A novel attribute-based access control scheme using blockchain for iot. IEEE Access, 7, 38431–38441.

    Google Scholar 

  94. Ronghua, Xu., Chen, Yu., Blasch, E., & Chen, G. (2018). Blendcac: A smart contract enabled decentralized capability-based access control mechanism for the IoT. Computers, 7(3), 39.

    Google Scholar 

  95. Ali, G., Ahmad, N., Cao, Y., Asif, M., Cruickshank, H., & Ali, Q. E. (2019). Blockchain based permission delegation and access control in internet of things (BACI). Computers & Security, 86, 318–334.

    Google Scholar 

  96. Yuta Nakamura, Yuanyu Zhang, Masahiro Sasabe, and Shoji Kasahara. (2019) Capability-based access control for the internet of things: an ethereum blockchain-based scheme. In 2019 IEEE Global Communications Conference (GLOBECOM), 1–6. IEEE

  97. Shuang Sun, Shudong Chen, Rong Du, Weiwei Li, and Donglin Qi. (2019). Blockchain based fine-grained and scalable access control for iot security and privacy. In 2019 IEEE Fourth International Conference on Data Science in Cyberspace (DSC), 598–603. IEEE

  98. Al-Naji, F. H., & Zagrouba, R. (2020). Cab-IoT: Continuous authentication architecture based on blockchain for Internet of Things. Journal of King Saud University-Computer and Information Sciences, 34(6), 2497–2514.

    Google Scholar 

  99. Guo, S., Xing, Hu., Guo, S., Qiu, X., & Qi, F. (2019). Blockchain meets edge computing: A distributed and trusted authentication system. IEEE Transactions on Industrial Informatics, 16(3), 1972–1983.

    Google Scholar 

  100. Mohammad El-Hajj, Ahmad Fadlallah, Maroun Chamoun, and Ahmed Serhrouchni. (2019). Ethereum for secure authentication of iot using pre-shared keys (psks). In 2019 International Conference on Wireless Networks and Mobile Communications (WINCOM), 1–7. IEEE.

  101. Cheng, G., Chen, Y., Deng, S., Gao, H., & Yin, J. (2021). A blockchain-based mutual authentication scheme for collaborative edge computing. IEEE Transactions on Computational Social Systems, 9, 146–158.

    Google Scholar 

  102. Yang, Q., & Wang, H. (2021). Privacy-preserving transactive energy management for IoT-aided smart homes via blockchain. IEEE Internet of Things Journal, 8, 11463.

    Google Scholar 

  103. Zhao, Q., Chen, S., Liu, Z., Baker, T., & Zhang, Y. (2020). Blockchain-based privacy-preserving remote data integrity checking scheme for iot information systems. Information Processing & Management, 57(6), 102355.

    Google Scholar 

  104. N Bhalaji, PC Abilashkumar, and S Aboorva. (2019). A blockchain based approach for privacy preservation in healthcare iot. In International Conference on Intelligent Computing and Communication Technologies, 465–473. Springer.

  105. Lv, P., Wang, L., Zhu, H., Deng, W., & Lize, Gu. (2019). An iot-oriented privacy-preserving publish/subscribe model over blockchains. IEEE Access, 7, 41309–41314.

    Google Scholar 

  106. Shen, M., Tang, X., Zhu, L., Xiaojiang, Du., & Guizani, M. (2019). Privacy-preserving support vector machine training over blockchain-based encrypted IoT data in smart cities. IEEE Internet of Things Journal, 6(5), 7702–7712.

    Google Scholar 

  107. Xiong, Z., Zhang, Y., Luong, N. C., Niyato, D., Wang, P., & Guizani, N. (2020). The best of both worlds: A general architecture for data management in blockchain-enabled internet-of-things. IEEE Network, 34(1), 166–173.

    Google Scholar 

  108. Zaheer, K., Ghafoor, A. A., & Pervez, Z. (2020). Blockchain and edge computing–based architecture for participatory smart city applications. Concurrency and Computation: Practice and Experience, 32, e5566.

    Google Scholar 

  109. Zhaofeng, Ma., Lingyun, W., Xiaochang, W., Zhen, W., & Weizhe, Z. (2019). Blockchain-enabled decentralized trust management and secure usage control of IoT big data. IEEE Internet of Things Journal, 7(5), 4000–4015.

    Google Scholar 

  110. Said El Kafhali, Chorouk Chahir, Mohamed Hanini, and Khaled Salah. (2019). Architecture to manage internet of things data using blockchain and fog computing. In Proceedings of the 4th International Conference on Big Data and Internet of Things, 1–8.

  111. Jiang, Y., Wang, C., Wang, Y., & Gao, L. (2019). A cross-chain solution to integrating multiple blockchains for IoT data management. Sensors, 19(9), 2042.

    Google Scholar 

  112. Tatsuhiro Fukuda and Kazumasa Omote. (2021). Efficient blockchain-based iot firmware update considering distribution incentives. In 2021 IEEE Conference on Dependable and Secure Computing (DSC), 1–8. IEEE.

  113. Meng-Hsuan Tsai, Yu-Cheng Hsu, and Nai-Wei Lo. (2020). An efficient blockchain-based firmware update framework for iot environment. In 2020 15th Asia Joint Conference on Information Security (AsiaJCIS), 121–127. IEEE.

  114. Samip Dhakal, Fehmi Jaafar, and Pavol Zavarsky. (2019). Private blockchain network for iot device firmware integrity verification and update. In 2019 IEEE 19th International Symposium on High Assurance Systems Engineering (HASE), 164–170. IEEE.

  115. Yohan, A., & Lo, N.-W. (2020). Fotb: A secure blockchain-based firmware update framework for IoT environment. International Journal of Information Security, 19(3), 257–278.

    Google Scholar 

  116. He, X., Alqahtani, S., Gamble, R., & Papa, M. (2019). Securing over-the-air IoT firmware updates using blockchain. In Proceedings of the International Conference on Omni-Layer Intelligent Systems, 164–171.

  117. Shanthamallu Uday, Shankar, Spanias Andreas, Tepedelenlioglu Cihan, and Mike Stanley. A brief survey of machine learning methods and their sensor and iot applications. In 2017 8th International Conference on Information, Intelligence, Systems Applications. IEEE.

  118. Rincy N Thomas and Gupta Roopam. (2020). A survey on machine learning approaches and its techniques. In 2020 IEEE International Students’ Conference on Electrical, Electronics and Computer Science (SCEECS), 1–6. IEEE.

  119. Kai, A., Peter, D. M., Miles, B., & Anthony, B. A. (2017). Deep reinforcement learning: A brief survey. IEEE Signal Processing Magazine, 34, 26–38.

    Google Scholar 

  120. Leslie, K., Pack, L. M., & Moore Andrew, W. (1996). Reinforcement learning: A survey. Journal of Artificial Intelligence Research, 4, 237–285.

    Google Scholar 

  121. Yau, K. L. A., Goh, H. G., & Chieng, D. (2015). Application of reinforcement learning to wireless sensor networks: Models and algorithms. Computing, 97, 1045–1075.

    MathSciNet  MATH  Google Scholar 

  122. F. Richard YuYing He. (2019). Deep reinforcement learning for wireless networks. (electronic) Springer Briefs in Electrical and Computer Engineering.

  123. Sharma, B., Rakesh, S., Akansha, S., & Krishnavir, S. (2020). Deep reinforcement learning for wireless network. Machine Learning and Cognitive Computing for Mobile Communications and Wireless Networks. https://doi.org/10.1002/9781119640554.ch3

    Article  Google Scholar 

  124. Kevin, Z. S., Ngan, L. H., Luub Khoa, V., Hien, N., & Nicholas, A. (2021). Deep reinforcement learning in medical imaging: A literature review. Medical Image Analysis, 73, 102193.

    Google Scholar 

  125. Gu, T., Abhishek, A., Fu, H., Zhang, H., Basu, D., & Mohapatra, P. (2020). Towards learning-automation iot attack detection through reinforcement learning. In 2020 IEEE 21st International Symposium on” A World of Wireless, Mobile and Multimedia Networks”(WoWMoM), 88–97. IEEE.

  126. Pham, T. A. Q., Hadjadj-Aoul, Y., & Outtagarts, A. (2019). Deep reinforcement learning based QoS-aware routing in knowledge-defined networking. In International Conference on Heterogeneous Networking for Quality, Reliability, Security and Robustness, 14–26. Springer.

  127. Liang, W., Huang, W., Long, J., Zhang, Ke., Li, K.-C., & Zhang, D. (2020). Deep reinforcement learning for resource protection and real-time detection in IoT environment. IEEE Internet of Things Journal, 7(7), 6392–6401.

    Google Scholar 

  128. Mohamed Shakeel, P., Baskar, S., Sarma Dhulipala, V. R., Mishra, S., & Jaber, M. M. (2018). Maintaining security and privacy in health care system using learning based deep-q-networks. Journal of Medical Systems, 42(10), 1–10.

    Google Scholar 

  129. Alkasassbeh, M., Khan, S., AL-Qerem, A., Choo, K.-K., Alauthman, M., & Aslam, N. (2020). An efficient reinforcement learning-based botnet detection approach. Journal of Network and Computer Applications, 150, 102479.

    Google Scholar 

  130. Lopez-Martin, M., Carro, B., & Sanchez-Esguevillas, A. (2020). Application of deep reinforcement learning to intrusion detection for supervised problems. Expert Systems with Applications, 141, 112963.

    Google Scholar 

  131. Ni, Z., & Paul, S. (2019). A multistage game in smart grid security: A reinforcement learning solution. IEEE transactions on neural networks and learning systems, 30(9), 2684–2695.

    MathSciNet  Google Scholar 

  132. Ma, X., & Shi, W. (2020). Aesmote: Adversarial reinforcement learning with smote for anomaly detection. IEEE Transactions on Network Science and Engineering, 8(2), 943–956.

    Google Scholar 

  133. Liu, Y., Dong, M., Ota, K., Li, J., & Wu, J. (2018).Deep reinforcement learning based smart mitigation of ddos flooding in software-defined networks. In 2018 IEEE 23rd International Workshop on Computer Aided Modeling and Design of Communication Links and Networks (CAMAD), 1–6. IEEE.

  134. Li, Y., Wang, X., Liu, D., Guo, Q., Liu, X., Zhang, J., & Yitao, Xu. (2019). On the performance of deep reinforcement learning-based anti-jamming method confronting intelligent jammer. Applied Sciences, 9(7), 1361.

    Google Scholar 

  135. Kamalakanta, S., Rupesh, E. S., Kumar, R., Bera, P., & Venu Mad-hav, Y. (2020). A context-aware robust intrusion detection system: A reinforcement learning-based approach. International Journal of Information Security, 19(6), 657–678.

    Google Scholar 

  136. Wang, Y., Liu, X., Wang, M., & Yu, Y. (2020). A hidden anti-jamming method based on deep reinforcement learning. Retrieved from arXiv preprint arXiv:2012.12448.

  137. Kurt, M. N., Ogundijo, O., Li, C., & Wang, X. (2018). Online cyber-attack detection in smart grid: A reinforcement learning approach. IEEE Transactions on Smart Grid, 10(5), 5174–5185.

    Google Scholar 

  138. Nguyen, D., Pathirana, P., Ding, M., & Seneviratne, A. (2021). Secure computation offloading in blockchain based IoT networks with deep reinforcement learning. IEEE Transactions on Network Science and Engineering, 8(4), 3192–3208.

    Google Scholar 

  139. Mengting Liu, F., Richard, Yu., Teng, Y., Leung, V. C. M., & Song, M. (2019). Performance optimization for blockchain-enabled industrial internet of things (IIoT) systems: A deep reinforcement learning approach. IEEE Transactions on Industrial Informatics, 15(6), 3559–3570.

    Google Scholar 

  140. Jie Feng, F., Richard, Yu., Pei, Q., Chu, X., Jianbo, Du., & Zhu, Li. (2019). Cooperative computation offloading and resource allocation for blockchain-enabled mobile-edge computing: A deep reinforcement learning approach. IEEE Internet of Things Journal, 7(7), 6214–6228.

    Google Scholar 

  141. Gao, Y., Wu, W., Nan, H., Sun, Y., & Si, P. (2020). Deep reinforcement learning based task scheduling in mobile blockchain for IoT applications. In ICC 2020–2020 IEEE International Conference on Communications (ICC), 1–7. IEEE.

  142. Mhaisen, N., Fetais, N., Erbad, A., Mohamed, A., & Guizani, M. (2020). To chain or not to chain: A reinforcement learning approach for blockchain-enabled IoT monitoring applications. Future Generation Computer Systems, 111, 39–51.

    Google Scholar 

  143. Xiao, L., Ding, Y., Jiang, D., Huang, J., Wang, D., Li, J., & Vincent Poor, H. (2020). A reinforcement learning and blockchain-based trust mechanism for edge networks. IEEE Transactions on Communications, 68(9), 5460–5470.

    Google Scholar 

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Gasmi, R., Hammoudi, S., Lamri, M. et al. Recent Reinforcement Learning and Blockchain Based Security Solutions for Internet of Things: Survey. Wireless Pers Commun 132, 1307–1345 (2023). https://doi.org/10.1007/s11277-023-10664-1

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