Review
BibTex RIS Cite

Year 2025, Volume: 8 Issue: 3, 570 - 591, 30.09.2025

Maysaa Salama

https://doi.org/10.35377/saucis...1582663

Abstract

References

  • B. Bitcoin et al., “Blockchain Technology,” 2015.
  • I. Latin and A. Transactions, “MQTT Protocol: Fundamentals, Tools and Future Directions,” 2019.
  • IEEE Staff, 2019 Amity International Conference on Artificial Intelligence (AICAI). IEEE, 2019.
  • S. Bhatnagar, “Integrated Blockchain and AI Research Infrastructure for IoT Based Applications,” in 2023 International Conference on Artificial Intelligence and Smart Communication, AISC 2023, Institute of Electrical and Electronics Engineers Inc., 2023, pp. 1140–1144. doi: 10.1109/AISC56616.2023.10085063.
  • S. Haque, Z. Eberhart, A. Bansal, and C. McMillan, “Semantic Similarity Metrics for Evaluating Source Code Summarization,” in IEEE International Conference on Program Comprehension, IEEE Computer Society, 2022, pp. 36–47. doi: 10.1145.
  • A. Miglani and N. Kumar, “Blockchain management and machine learning adaptation for IoT environment in 5G and beyond networks: A systematic review,” Comput Commun, vol. 178, pp. 37–63, Oct. 2021, doi: 10.1016/j.comcom.2021.07.009.
  • A. Outchakoucht and J. P. Leroy, “Dynamic Access Control Policy based on Blockchain and Machine Learning for the Internet of Things,” 2017. [Online]. Available: www.ijacsa.thesai.org
  • S. Saxena, S. Khare, and S. Pal, “A Blockchain and Machine Learning based IoT Framework to Improve Contract Farming,” in 2021 IEEE Globecom Workshops, GC Wkshps 2021 - Proceedings, Institute of Electrical and Electronics Engineers Inc., 2021. doi: 10.1109/GCWkshps52748.2021.9682083.
  • C. Wan, A. Mehmood, M. Carsten, G. Epiphaniou, and J. Lloret, “A Blockchain Based Forensic System for IoT Sensors using MQTT Protocol,” in 2022 9th International Conference on Internet of Things, Systems, Management and Security, IOTSMS 2022, Institute of Electrical and Electronics Engineers Inc., 2022. doi: 10.1109/IOTSMS58070.2022.10062190.
  • A. T., S. Babu, and B. S. Manoj, “A Machine Learning Consensus Based Light-Weight Blockchain Architecture for Internet of Things,” in 2022 14th International Conference on COMmunication Systems and NETworkS, COMSNETS 2022, Institute of Electrical and Electronics Engineers Inc., 2022, pp. 1–6. doi: 10.1109/COMSNETS53615.2022.9668487.
  • V. A. Athavale, A. Bansal, S. Nalajala, and S. Aurelia, “WITHDRAWN: Integration of blockchain and IoT for data storage and management,” Mater Today Proc, Oct. 2020, doi: 10.1016/j.matpr.2020.09.643.
  • A. Dixit, A. Trivedi, and W. W. Godfrey, “IoT and Machine Learning based Peer to Peer Framework for Employee Attendance System using Blockchain,” in Proceedings - International Conference on Augmented Intelligence and Sustainable Systems, ICAISS 2022, Institute of Electrical and Electronics Engineers Inc., 2022, pp. 1088–1093. doi: 10.1109/ICAISS55157.2022.10010846.
  • S. C. Ch, S. Puli, K. Lakshmi Viveka, and M. V. B. T. Santhi, “Machine Learning Based Data Security Model Using Blockchain for Secure Data Transmission in IoT,” in Proceedings of the 2nd International Conference on Electronics and Sustainable Communication Systems, ICESC 2021, Institute of Electrical and Electronics Engineers Inc., Aug. 2021, pp. 1521–1527. doi: 10.1109/ICESC51422.2021.9532659.
  • P. Kumar et al., “PPSF: A Privacy-Preserving and Secure Framework Using Blockchain-Based Machine-Learning for IoT-Driven Smart Cities,” IEEE Trans Netw Sci Eng, vol. 8, no. 3, pp. 2326–2341, Jul. 2021, doi: 10.1109/TNSE.2021.3089435.
  • A. F. M. Suaib Akhter, M. Ahmed, A. F. M. Shahen Shah, A. Anwar, A. S. M. Kayes, and A. Zengin, “A blockchain-based authentication protocol for cooperative vehicular ad hoc network,” Sensors (Switzerland), vol. 21, no. 4, pp. 1–21, Feb. 2021, doi: 10.3390/s21041273.
  • D. D. Datiri and M. Li, “A Cluster enabled Blockchain-based Data management for IoT systems,” in Proceedings of the 2023 24th International Carpathian Control Conference, ICCC 2023, Institute of Electrical and Electronics Engineers Inc., 2023, pp. 88–92. doi: 10.1109/ICCC57093.2023.10178949.
  • C. Yiyang and K. Takashio, “A Floating Calculation Revamp For the Ethereum Blockchain-Based IoT Systems,” in 2022 IEEE 8th World Forum on Internet of Things, WF-IoT 2022, Institute of Electrical and Electronics Engineers Inc., 2022. doi: 10.1109/WF-IoT54382.2022.10152068.
  • K. Kumari and M. Kumar Murmu, “A Leader Election Algorithm Using Blockchain for IoT”, doi: 10.1109/AIC.2023.135.
  • R. Michalski, D. Dziubaltowska, and P. MacEk, “Revealing the Character of Nodes in a Blockchain with Supervised Learning,” IEEE Access, vol. 8, pp. 109639–109647, 2020, doi: 10.1109/ACCESS.2020.3001676.
  • R. Akkaoui, A. Stefanov, P. Palensky, and D. H. J. Epema, “Resilient, Auditable and Secure IoT-Enabled Smart Inverter Firmware Amendments With Blockchain,” IEEE Internet Things J, pp. 1–1, 2023, doi: 10.1109/JIOT.2023.3321954.
  • S. P. J, A. S, U. ranee L, T. F. A, M. S, and M. S, “Revolutionizing Industries Through IoT, Blockchain and AI Integration,” in 2023 3rd International Conference on Pervasive Computing and Social Networking (ICPCSN), IEEE, Jun. 2023, pp. 972–977. doi: 10.1109/ICPCSN58827.2023.00166.
  • A. Sumarudin et al., “Implementation of IoT Sensored Data Integrity for Irrigation in Precision Agriculture Using Blockchain Ethereum,” in 2022 5th International Seminar on Research of Information Technology and Intelligent Systems, ISRITI 2022, Institute of Electrical and Electronics Engineers Inc., 2022, pp. 29–33. doi: 10.1109/ISRITI56927.2022.10052902.
  • J. P. De Brito Goncalves, G. Spelta, R. Da Silva Villaca, and R. L. Gomes, “IoT Data Storage on a Blockchain Using Smart Contracts and IPFS,” in Proceedings - 2022 IEEE International Conference on Blockchain, Blockchain 2022, Institute of Electrical and Electronics Engineers Inc., 2022, pp. 508–511. doi: 10.1109/Blockchain55522.2022.00078.
  • F. Zerka et al., “Blockchain for privacy preserving and trustworthy distributed machine learning in multicentric medical imaging (C-DistriM),” IEEE Access, vol. 8, pp. 183939–183951, 2020, doi: 10.1109/ACCESS.2020.3029445.
  • N. El Akrami, M. Hanine, E. S. Flores, D. G. Aray, and I. Ashraf, “Unleashing the Potential of Blockchain and Machine Learning: Insights and Emerging Trends From Bibliometric Analysis,” IEEE Access, vol. 11, pp. 78879–78903, 2023, doi: 10.1109/ACCESS.2023.3298371.
  • T. H. Pranto, K. T. A. M. Hasib, T. Rahman, A. B. Haque, A. K. M. N. Islam, and R. M. Rahman, “Blockchain and Machine Learning for Fraud Detection: A Privacy-Preserving and Adaptive Incentive Based Approach,” IEEE Access, vol. 10, pp. 87115–87134, 2022, doi: 10.1109/ACCESS.2022.3198956.
  • Q. Zhou, K. Zheng, K. Zhang, L. Hou, and X. Wang, “Vulnerability Analysis of Smart Contract for Blockchain-Based IoT Applications: A Machine Learning Approach,” IEEE Internet Things J, vol. 9, no. 24, pp. 24695–24707, Dec. 2022, doi: 10.1109/JIOT.2022.3196269.
  • Z. Shahbazi and Y. C. Byun, “Blockchain-Based Event Detection and Trust Verification Using Natural Language Processing and Machine Learning,” IEEE Access, vol. 10, pp. 5790–5800, 2022, doi: 10.1109/ACCESS.2021.3139586.
  • T. R. Gadekallu, M. M K, S. K. S, N. Kumar, S. Hakak, and S. Bhattacharya, “Blockchain-Based Attack Detection on Machine Learning Algorithms for IoT-Based e-Health Applications,” IEEE Internet of Things Magazine, vol. 4, no. 3, pp. 30–33, Sep. 2021, doi: 10.1109/iotm.1021.2000160.
  • S. V. Sanghami, J. J. Lee, and Q. Hu, “Machine-Learning-Enhanced Blockchain Consensus With Transaction Prioritization for Smart Cities,” IEEE Internet Things J, vol. 10, no. 8, pp. 6661–6672, Apr. 2023, doi: 10.1109/JIOT.2022.3175208.
  • A. S. Khan, X. Zhang, S. Lambotharan, G. Zheng, B. Assadhan, and L. Hanzo, “Machine Learning Aided Blockchain Assisted Framework for Wireless Networks,” IEEE Netw, vol. 34, no. 5, pp. 262–268, Sep. 2020, doi: 10.1109/MNET.011.1900643.
  • A. P. Kalapaaking, I. Khalil, M. S. Rahman, M. Atiquzzaman, X. Yi, and M. Almashor, “Blockchain-Based Federated Learning With Secure Aggregation in Trusted Execution Environment for Internet-of-Things,” IEEE Trans Industr Inform, vol. 19, no. 2, pp. 1703–1714, Feb. 2023, doi: 10.1109/TII.2022.3170348.
  • Y. Qu, S. R. Pokhrel, S. Garg, L. Gao, and Y. Xiang, “A Blockchained Federated Learning Framework for Cognitive Computing in Industry 4.0 Networks,” IEEE Trans Industr Inform, vol. 17, no. 4, pp. 2964–2973, Apr. 2021, doi: 10.1109/TII.2020.3007817.
  • M. Li, F. R. Yu, P. Si, W. Wu, and Y. Zhang, “Resource Optimization for Delay-Tolerant Data in Blockchain-Enabled IoT with Edge Computing: A Deep Reinforcement Learning Approach,” IEEE Internet Things J, vol. 7, no. 10, pp. 9399–9412, Oct. 2020, doi: 10.1109/JIOT.2020.3007869.
  • T. Vaiyapuri, K. Shankar, S. Rajendran, S. Kumar, S. Acharya, and H. Kim, “Blockchain Assisted Data Edge Verification With Consensus Algorithm for Machine Learning Assisted IoT,” IEEE Access, vol. 11, pp. 55370–55379, 2023, doi: 10.1109/ACCESS.2023.3280798.
  • C. Qiu, X. Wang, H. Yao, J. Du, F. R. Yu, and S. Guo, “Networking Integrated Cloud-Edge-End in IoT: A Blockchain-Assisted Collective Q-Learning Approach,” IEEE Internet Things J, vol. 8, no. 16, pp. 12694–12704, Aug. 2021, doi: 10.1109/JIOT.2020.3007650.
  • S. Tanwar, Q. Bhatia, P. Patel, A. Kumari, P. K. Singh, and W. C. Hong, “Machine Learning Adoption in Blockchain-Based Smart Applications: The Challenges, and a Way Forward,” IEEE Access, vol. 8, pp. 474–448, 2020, doi: 10.1109/ACCESS.2019.2961372.
  • H. A. Madni, R. M. Umer, and G. L. Foresti, “Blockchain-Based Swarm Learning for the Mitigation of Gradient Leakage in Federated Learning,” IEEE Access, vol. 11, pp. 16549–16556, 2023, doi: 10.1109/ACCESS.2023.3246126.
  • S. Pandey et al., “Do-It-Yourself Recommender System: Reusing and Recycling With Blockchain and Deep Learning,” IEEE Access, vol. 10, pp. 90056–90067, 2022, doi: 10.1109/ACCESS.2022.3199661.
  • H. Kim, S. H. Kim, J. Y. Hwang, and C. Seo, “Efficient privacy-preserving machine learning for blockchain network,” IEEE Access, vol. 7, pp. 136481–136495, 2019, doi: 10.1109/ACCESS.2019.2940052.
  • “Z. Huang, F. Liu, M. Tang, J. Qiu, Y. Peng, “A Distributed Computing Framework Based on Lightweight Variance Reduction Method to Accelerate Machine Learning Training on Blockchain,” China Communications, vol. 17, no. 9, pp. 77-89, Sep. 2020, doi: 10.23919/JCC.2020.09.007”.
  • M. Ghafourian et al., “Combining Blockchain and Biometrics: A Survey on Technical Aspects and a First Legal Analysis,” Feb. 2023, [Online]. Available: http://arxiv.org/abs/2302.10883
  • T. Hewa, M. Ylianttila, and M. Liyanage, “Survey on blockchain based smart contracts: Applications, opportunities and challenges,” Journal of Network and Computer Applications, vol. 177. Academic Press, Mar. 01, 2021. doi: 10.1016/j.jnca.2020.102857.
  • D. Huang, C. J. Chung, Q. Dong, J. Luo, and M. Kang, “Building private blockchains over public blockchains (POP): An attribute-based access control approach,” in Proceedings of the ACM Symposium on Applied Computing, Association for Computing Machinery, 2019, pp. 355–363. doi: 10.1145/3297280.3297317.
  • T. Ncube, N. Dlodlo, and A. Terzoli, “Private Blockchain Networks: A Solution for Data Privacy,” in 2020 2nd International Multidisciplinary Information Technology and Engineering Conference, IMITEC 2020, Institute of Electrical and Electronics Engineers Inc., Nov. 2020. doi: 10.1109/IMITEC50163.2020.9334132.
  • O. Dib, A. Durand, K.-L. Brousmiche, E. Thea, and B. Hamida, “Consortium Blockchains: Overview, Applications and Challenges,” 2018. [Online]. Available: http://www.iariajournals.org/telecommunications/2018,
  • M. Singh, M. A. Rajan, V. L. Shivraj, and P. Balamuralidhar, “Secure MQTT for Internet of Things (IoT),” in Proceedings - 2015 5th International Conference on Communication Systems and Network Technologies, CSNT 2015, Institute of Electrical and Electronics Engineers Inc., Sep. 2015, pp. 746–751. doi: 10.1109/CSNT.2015.16.
  • IEEE Systems Council and Institute of Electrical and Electronics Engineers, ISSE 2017 : 2017 IEEE International Symposium on Systems Engineering : Vienna, Austria, October 11-13, 2017 : 2017 symposium proceedings.
  • F. ARTKIN, “Applications of Artificial Intelligence in Mechanical Engineering,” European Journal of Science and Technology, Dec. 2022, doi: 10.31590/ejosat.1224045.
  • Y. Liu, F. R. Yu, X. Li, H. Ji, and V. C. M. Leung, “Blockchain and Machine Learning for Communications and Networking Systems,” IEEE Communications Surveys and Tutorials, vol. 22, no. 2, pp. 1392–1431, Apr. 2020, doi: 10.1109/COMST.2020.2975911.
  • A. Singh, “Bharati Vidyapeeth’s Institute of Computer Applications and Management (BVICAM),” 2016.
  • S. Naeem, A. Ali, S. Anam, and M. M. Ahmed, “An Unsupervised Machine Learning Algorithms: Comprehensive Review,” International Journal of Computing and Digital Systems, vol. 13, no. 1, pp. 911–921, 2023, doi: 10.12785/ijcds/130172.
  • T. P. Lillicrap et al., “Continuous control with deep reinforcement learning,” Sep. 2015, [Online]. Available: http://arxiv.org/abs/1509.02971
  • A. Alzahrani and T. H. H. Aldhyani, “Artificial Intelligence Algorithms for Detecting and Classifying MQTT Protocol Internet of Things Attacks,” Electronics (Switzerland), vol. 11, no. 22, Nov. 2022, doi: 10.3390/electronics11223837.
  • M. Abdelrazig Abubakar, Z. Jaroucheh, A. Al-Dubai, and X. Liu, “Blockchain-based identity and authentication scheme for MQTT protocol,” in ACM International Conference Proceeding Series, Association for Computing Machinery, Mar. 2021, pp. 73–81. doi: 10.1145/3460537.3460549.
  • F. Buccafurri, V. De Angelis, and R. Nardone, “Securing MQTT by blockchain-based otp authentication,” Sensors (Switzerland), vol. 20, no. 7, Apr. 2020, doi: 10.3390/s20072002.
  • G. Kalele, “An In-Depth Examination of Traditional, Blockchain, and AI-Based Key-Security for The Cyber-Physical IoT Networks,” Institute of Electrical and Electronics Engineers (IEEE), Jul. 2023, pp. 2004–2008. doi: 10.1109/icacite57410.2023.10182722.
  • N. Adhikari and M. Ramkumar, “IoT and Blockchain Integration: Applications, Opportunities, and Challenges,” Network, vol. 3, no. 1, pp. 115–141, Mar. 2023, doi: 10.3390/network3010006.
  • U. Majeed, L. U. Khan, I. Yaqoob, S. M. A. Kazmi, K. Salah, and C. S. Hong, “Blockchain for IoT-based smart cities: Recent advances, requirements, and future challenges,” Journal of Network and Computer Applications, vol. 181. Academic Press, May 01, 2021. doi: 10.1016/j.jnca.2021.103007.
  • S. Menon et al., “Blockchain and Machine Learning Inspired Secure Smart Home Communication Network,” Sensors, vol. 23, no. 13, Jul. 2023, doi: 10.3390/s23136132.
  • K. Nethravathi, A. Tiwari, D. Uike, R. Jaiswal, and K. Pant, “Applications of Artificial Intelligence and Blockchain Technology in Improved Supply Chain Financial Risk Management,” in Proceedings of 5th International Conference on Contemporary Computing and Informatics, IC3I 2022, Institute of Electrical and Electronics Engineers Inc., 2022, pp. 242–246. doi: 10.1109/IC3I56241.2022.10072787.
  • M. Alazab, S. Alhyari, A. Awajan, and A. B. Abdallah, “Blockchain technology in supply chain management: an empirical study of the factors affecting user adoption/acceptance,” Cluster Comput, vol. 24, no. 1, pp. 83–101, Mar. 2021, doi: 10.1007/s10586-020-03200-4.
  • M. Hader, A. Elmhamedi, and A. Abouabdellah, “Blockchain technology in supply chain management and loyalty programs: Toward blockchain implementation in retail market,” in 2020 13th International Colloquium of Logistics and Supply Chain Management, LOGISTIQUA 2020, Institute of Electrical and Electronics Engineers Inc., Dec. 2020. doi: 10.1109/LOGISTIQUA49782.2020.9353879.
  • I. Lahlou and N. Motaki, “Integrating Blockchain with ERP systems for better supply chain performance,” in 2022 IEEE 14th International Conference of Logistics and Supply Chain Management, LOGISTIQUA 2022, Institute of Electrical and Electronics Engineers Inc., 2022. doi: 10.1109/LOGISTIQUA55056.2022.9938086.
  • G. K. Singh and M. Dadhich, “Supply Chain Management Growth With the Adoption of Blockchain Technology (BoT) and Internet of Things (IoT),” Institute of Electrical and Electronics Engineers (IEEE), Jul. 2023, pp. 321–325. doi: 10.1109/icacite57410.2023.10182619.
  • Y. Khaoua, Y. Mouzouna, J. Arif, F. Jawab, and M. Azari, “The Contribution of Blockchain Technology in the Supply Chain Management: The Shipping Industry as an Example,” in 2022 IEEE 14th International Conference of Logistics and Supply Chain Management, LOGISTIQUA 2022, Institute of Electrical and Electronics Engineers Inc., 2022. doi: 10.1109/LOGISTIQUA55056.2022.9938046.
  • M. M. Salim, L. Park, and J. H. Park, “A Machine Learning based Scalable Blockchain architecture for a secure Healthcare system,” in International Conference on ICT Convergence, IEEE Computer Society, 2022, pp. 2231–2234. doi: 10.1109/ICTC55196.2022.9952962.
  • M. J. J. Gul, A. Paul, S. Rho, and M. Kim, “Blockchain based healthcare system with Artificial Intelligence,” in Proceedings - 2020 International Conference on Computational Science and Computational Intelligence, CSCI 2020, Institute of Electrical and Electronics Engineers Inc., Dec. 2020, pp. 740–741. doi: 10.1109/CSCI51800.2020.00138.
  • A. Haddad, M. H. Habaebi, M. R. Islam, and S. A. Zabidi, “Blockchain for Healthcare Medical Records Management System with Sharing Control,” in 2021 IEEE 7th International Conference on Smart Instrumentation, Measurement and Applications, ICSIMA 2021, Institute of Electrical and Electronics Engineers Inc., Aug. 2021, pp. 30–34. doi: 10.1109/ICSIMA50015.2021.9526301.
  • Wajiha and S. R. Patil, “Implementing Blockchain Technology in Healthcare Systems utilizing Machine learning Techniques,” in 2022 IEEE North Karnataka Subsection Flagship International Conference, NKCon 2022, Institute of Electrical and Electronics Engineers Inc., 2022. doi: 10.1109/NKCon56289.2022.10126874.
  • Global IT Research Institute, IEEE Communications Society, and Institute of Electrical and Electronics Engineers, The 23rd International Conference on Advanced Communications Technology : “On-Line security in Pandemic Era!” : Phoenix Park, Pyeongchang, Korea (South), (On-line Conference), Feb. 07-10, 2021 : proceeding & journal.
  • J. Dargan, N. Gupta, and L. Singh, “Blockchain Based Energy Management System: A Proposed Model,” in Proceedings of International Conference on Technological Advancements and Innovations, ICTAI 2021, Institute of Electrical and Electronics Engineers Inc., 2021, pp. 510–514. doi: 10.1109/ICTAI53825.2021.9673233.
  • A. Bin Masood, S. Javaid, Y. Tan, V. Vassiliou, and M. Lestas, “A Blockchain-Based Transactive Energy Management Scheme for Nano-Grids using Power Flow Coloring,” Institute of Electrical and Electronics Engineers (IEEE), Aug. 2023, pp. 187–188. doi: 10.1109/icce-taiwan58799.2023.10226681.
  • A. Jayavarma, Preetha, and M. G. Nair, “A secure energy trading in a smart community by integrating Blockchain and machine learning approach,” Smart Science, 2023, doi: 10.1080/23080477.2023.2270820.
  • F. Mohammadi, M. Sanjari, and M. Saif, “A Real-Time Blockchain-Based State Estimation System for Battery Energy Storage Systems,” in 2022 IEEE Kansas Power and Energy Conference, KPEC 2022, Institute of Electrical and Electronics Engineers Inc., 2022. doi: 10.1109/KPEC54747.2022.9814731.
  • I. Demidov, H. Dibaba, A. Pinomaa, S. Honkapuro, and M. Nieminen, “Energy Management System for Community-Centered Off-Grid System with a Blockchain-Based P2P Energy Market,” in International Conference on the European Energy Market, EEM, IEEE Computer Society, 2023. doi: 10.1109/EEM58374.2023.10161848.
  • T. Cai, J. Wu, C. Yu, and V. Brusic, “Blockchain with Machine Learning for Financial Portfolio Management,” in ICEIEC 2023 - Proceedings of 2023 IEEE 13th International Conference on Electronics, Information and Emergency Communication, Institute of Electrical and Electronics Engineers Inc., 2023, pp. 255–259. doi: 10.1109/ICEIEC58029.2023.10201043.
  • P. Sudha, J. J. Amalraj, and M. Sivakumar, “Lion Swarm Optimization with Deep Learning Driven Predictive Model on Blockchain Financial Product Return Rates,” in Proceedings of the 2023 2nd International Conference on Electronics and Renewable Systems, ICEARS 2023, Institute of Electrical and Electronics Engineers Inc., 2023, pp. 1076–1080. doi: 10.1109/ICEARS56392.2023.10085579.
  • J. Chen, “Research on the Application of Blockchain Technology in Supply Chain Financial Business,” in Proceedings - 2020 2nd International Conference on Applied Machine Learning, ICAML 2020, Institute of Electrical and Electronics Engineers Inc., 2020, pp. 371–374. doi: 10.1109/ICAML51583.2020.00081.

Integrating Blockchain, MQTT, and Machine Learning for Enhanced IoT Applications: A Comprehensive Survey

Year 2025, Volume: 8 Issue: 3, 570 - 591, 30.09.2025

Maysaa Salama

https://doi.org/10.35377/saucis...1582663

Abstract

This extensive research investigates the integration of blockchain, MQTT and machine learning on the Internet of Things (IoT), a field ripe for transformation with technologies. These three technologies are blockchain and Message Queuing Telemetry Transport (MQTT). Machine learning is a foundational pillar, each offering unique benefits to enhance data exchange, security and decision making in interconnected IoT environments. Our study aims to explore the synergies among these technologies and the implications of their combined usage on the IoT. I delve into how their integration strengthens data security, enables communication, and facilitates data-driven decision-making across IoT scenarios. The study examines types of blockchain technology and the significance of MQTT in IoT communication. Additionally, I explore the implementation of machine learning models. Our primary focus is on exploring how combining blockchain and MQTT can enhance data sharing. I address challenges such as privacy concerns, scalability issues and consensus processes. To illustrate the impact of this convergence, I present practical examples from industries like supply chain management, healthcare services, and finance. Furthermore, this research also encompasses themes such as interoperability, among systems standardization measures, edge computing applications, and privacy-oriented machine learning approaches.

Keywords

Blockchain MQTT Machine Learning Internet of Things.

References

  • B. Bitcoin et al., “Blockchain Technology,” 2015.
  • I. Latin and A. Transactions, “MQTT Protocol: Fundamentals, Tools and Future Directions,” 2019.
  • IEEE Staff, 2019 Amity International Conference on Artificial Intelligence (AICAI). IEEE, 2019.
  • S. Bhatnagar, “Integrated Blockchain and AI Research Infrastructure for IoT Based Applications,” in 2023 International Conference on Artificial Intelligence and Smart Communication, AISC 2023, Institute of Electrical and Electronics Engineers Inc., 2023, pp. 1140–1144. doi: 10.1109/AISC56616.2023.10085063.
  • S. Haque, Z. Eberhart, A. Bansal, and C. McMillan, “Semantic Similarity Metrics for Evaluating Source Code Summarization,” in IEEE International Conference on Program Comprehension, IEEE Computer Society, 2022, pp. 36–47. doi: 10.1145.
  • A. Miglani and N. Kumar, “Blockchain management and machine learning adaptation for IoT environment in 5G and beyond networks: A systematic review,” Comput Commun, vol. 178, pp. 37–63, Oct. 2021, doi: 10.1016/j.comcom.2021.07.009.
  • A. Outchakoucht and J. P. Leroy, “Dynamic Access Control Policy based on Blockchain and Machine Learning for the Internet of Things,” 2017. [Online]. Available: www.ijacsa.thesai.org
  • S. Saxena, S. Khare, and S. Pal, “A Blockchain and Machine Learning based IoT Framework to Improve Contract Farming,” in 2021 IEEE Globecom Workshops, GC Wkshps 2021 - Proceedings, Institute of Electrical and Electronics Engineers Inc., 2021. doi: 10.1109/GCWkshps52748.2021.9682083.
  • C. Wan, A. Mehmood, M. Carsten, G. Epiphaniou, and J. Lloret, “A Blockchain Based Forensic System for IoT Sensors using MQTT Protocol,” in 2022 9th International Conference on Internet of Things, Systems, Management and Security, IOTSMS 2022, Institute of Electrical and Electronics Engineers Inc., 2022. doi: 10.1109/IOTSMS58070.2022.10062190.
  • A. T., S. Babu, and B. S. Manoj, “A Machine Learning Consensus Based Light-Weight Blockchain Architecture for Internet of Things,” in 2022 14th International Conference on COMmunication Systems and NETworkS, COMSNETS 2022, Institute of Electrical and Electronics Engineers Inc., 2022, pp. 1–6. doi: 10.1109/COMSNETS53615.2022.9668487.
  • V. A. Athavale, A. Bansal, S. Nalajala, and S. Aurelia, “WITHDRAWN: Integration of blockchain and IoT for data storage and management,” Mater Today Proc, Oct. 2020, doi: 10.1016/j.matpr.2020.09.643.
  • A. Dixit, A. Trivedi, and W. W. Godfrey, “IoT and Machine Learning based Peer to Peer Framework for Employee Attendance System using Blockchain,” in Proceedings - International Conference on Augmented Intelligence and Sustainable Systems, ICAISS 2022, Institute of Electrical and Electronics Engineers Inc., 2022, pp. 1088–1093. doi: 10.1109/ICAISS55157.2022.10010846.
  • S. C. Ch, S. Puli, K. Lakshmi Viveka, and M. V. B. T. Santhi, “Machine Learning Based Data Security Model Using Blockchain for Secure Data Transmission in IoT,” in Proceedings of the 2nd International Conference on Electronics and Sustainable Communication Systems, ICESC 2021, Institute of Electrical and Electronics Engineers Inc., Aug. 2021, pp. 1521–1527. doi: 10.1109/ICESC51422.2021.9532659.
  • P. Kumar et al., “PPSF: A Privacy-Preserving and Secure Framework Using Blockchain-Based Machine-Learning for IoT-Driven Smart Cities,” IEEE Trans Netw Sci Eng, vol. 8, no. 3, pp. 2326–2341, Jul. 2021, doi: 10.1109/TNSE.2021.3089435.
  • A. F. M. Suaib Akhter, M. Ahmed, A. F. M. Shahen Shah, A. Anwar, A. S. M. Kayes, and A. Zengin, “A blockchain-based authentication protocol for cooperative vehicular ad hoc network,” Sensors (Switzerland), vol. 21, no. 4, pp. 1–21, Feb. 2021, doi: 10.3390/s21041273.
  • D. D. Datiri and M. Li, “A Cluster enabled Blockchain-based Data management for IoT systems,” in Proceedings of the 2023 24th International Carpathian Control Conference, ICCC 2023, Institute of Electrical and Electronics Engineers Inc., 2023, pp. 88–92. doi: 10.1109/ICCC57093.2023.10178949.
  • C. Yiyang and K. Takashio, “A Floating Calculation Revamp For the Ethereum Blockchain-Based IoT Systems,” in 2022 IEEE 8th World Forum on Internet of Things, WF-IoT 2022, Institute of Electrical and Electronics Engineers Inc., 2022. doi: 10.1109/WF-IoT54382.2022.10152068.
  • K. Kumari and M. Kumar Murmu, “A Leader Election Algorithm Using Blockchain for IoT”, doi: 10.1109/AIC.2023.135.
  • R. Michalski, D. Dziubaltowska, and P. MacEk, “Revealing the Character of Nodes in a Blockchain with Supervised Learning,” IEEE Access, vol. 8, pp. 109639–109647, 2020, doi: 10.1109/ACCESS.2020.3001676.
  • R. Akkaoui, A. Stefanov, P. Palensky, and D. H. J. Epema, “Resilient, Auditable and Secure IoT-Enabled Smart Inverter Firmware Amendments With Blockchain,” IEEE Internet Things J, pp. 1–1, 2023, doi: 10.1109/JIOT.2023.3321954.
  • S. P. J, A. S, U. ranee L, T. F. A, M. S, and M. S, “Revolutionizing Industries Through IoT, Blockchain and AI Integration,” in 2023 3rd International Conference on Pervasive Computing and Social Networking (ICPCSN), IEEE, Jun. 2023, pp. 972–977. doi: 10.1109/ICPCSN58827.2023.00166.
  • A. Sumarudin et al., “Implementation of IoT Sensored Data Integrity for Irrigation in Precision Agriculture Using Blockchain Ethereum,” in 2022 5th International Seminar on Research of Information Technology and Intelligent Systems, ISRITI 2022, Institute of Electrical and Electronics Engineers Inc., 2022, pp. 29–33. doi: 10.1109/ISRITI56927.2022.10052902.
  • J. P. De Brito Goncalves, G. Spelta, R. Da Silva Villaca, and R. L. Gomes, “IoT Data Storage on a Blockchain Using Smart Contracts and IPFS,” in Proceedings - 2022 IEEE International Conference on Blockchain, Blockchain 2022, Institute of Electrical and Electronics Engineers Inc., 2022, pp. 508–511. doi: 10.1109/Blockchain55522.2022.00078.
  • F. Zerka et al., “Blockchain for privacy preserving and trustworthy distributed machine learning in multicentric medical imaging (C-DistriM),” IEEE Access, vol. 8, pp. 183939–183951, 2020, doi: 10.1109/ACCESS.2020.3029445.
  • N. El Akrami, M. Hanine, E. S. Flores, D. G. Aray, and I. Ashraf, “Unleashing the Potential of Blockchain and Machine Learning: Insights and Emerging Trends From Bibliometric Analysis,” IEEE Access, vol. 11, pp. 78879–78903, 2023, doi: 10.1109/ACCESS.2023.3298371.
  • T. H. Pranto, K. T. A. M. Hasib, T. Rahman, A. B. Haque, A. K. M. N. Islam, and R. M. Rahman, “Blockchain and Machine Learning for Fraud Detection: A Privacy-Preserving and Adaptive Incentive Based Approach,” IEEE Access, vol. 10, pp. 87115–87134, 2022, doi: 10.1109/ACCESS.2022.3198956.
  • Q. Zhou, K. Zheng, K. Zhang, L. Hou, and X. Wang, “Vulnerability Analysis of Smart Contract for Blockchain-Based IoT Applications: A Machine Learning Approach,” IEEE Internet Things J, vol. 9, no. 24, pp. 24695–24707, Dec. 2022, doi: 10.1109/JIOT.2022.3196269.
  • Z. Shahbazi and Y. C. Byun, “Blockchain-Based Event Detection and Trust Verification Using Natural Language Processing and Machine Learning,” IEEE Access, vol. 10, pp. 5790–5800, 2022, doi: 10.1109/ACCESS.2021.3139586.
  • T. R. Gadekallu, M. M K, S. K. S, N. Kumar, S. Hakak, and S. Bhattacharya, “Blockchain-Based Attack Detection on Machine Learning Algorithms for IoT-Based e-Health Applications,” IEEE Internet of Things Magazine, vol. 4, no. 3, pp. 30–33, Sep. 2021, doi: 10.1109/iotm.1021.2000160.
  • S. V. Sanghami, J. J. Lee, and Q. Hu, “Machine-Learning-Enhanced Blockchain Consensus With Transaction Prioritization for Smart Cities,” IEEE Internet Things J, vol. 10, no. 8, pp. 6661–6672, Apr. 2023, doi: 10.1109/JIOT.2022.3175208.
  • A. S. Khan, X. Zhang, S. Lambotharan, G. Zheng, B. Assadhan, and L. Hanzo, “Machine Learning Aided Blockchain Assisted Framework for Wireless Networks,” IEEE Netw, vol. 34, no. 5, pp. 262–268, Sep. 2020, doi: 10.1109/MNET.011.1900643.
  • A. P. Kalapaaking, I. Khalil, M. S. Rahman, M. Atiquzzaman, X. Yi, and M. Almashor, “Blockchain-Based Federated Learning With Secure Aggregation in Trusted Execution Environment for Internet-of-Things,” IEEE Trans Industr Inform, vol. 19, no. 2, pp. 1703–1714, Feb. 2023, doi: 10.1109/TII.2022.3170348.
  • Y. Qu, S. R. Pokhrel, S. Garg, L. Gao, and Y. Xiang, “A Blockchained Federated Learning Framework for Cognitive Computing in Industry 4.0 Networks,” IEEE Trans Industr Inform, vol. 17, no. 4, pp. 2964–2973, Apr. 2021, doi: 10.1109/TII.2020.3007817.
  • M. Li, F. R. Yu, P. Si, W. Wu, and Y. Zhang, “Resource Optimization for Delay-Tolerant Data in Blockchain-Enabled IoT with Edge Computing: A Deep Reinforcement Learning Approach,” IEEE Internet Things J, vol. 7, no. 10, pp. 9399–9412, Oct. 2020, doi: 10.1109/JIOT.2020.3007869.
  • T. Vaiyapuri, K. Shankar, S. Rajendran, S. Kumar, S. Acharya, and H. Kim, “Blockchain Assisted Data Edge Verification With Consensus Algorithm for Machine Learning Assisted IoT,” IEEE Access, vol. 11, pp. 55370–55379, 2023, doi: 10.1109/ACCESS.2023.3280798.
  • C. Qiu, X. Wang, H. Yao, J. Du, F. R. Yu, and S. Guo, “Networking Integrated Cloud-Edge-End in IoT: A Blockchain-Assisted Collective Q-Learning Approach,” IEEE Internet Things J, vol. 8, no. 16, pp. 12694–12704, Aug. 2021, doi: 10.1109/JIOT.2020.3007650.
  • S. Tanwar, Q. Bhatia, P. Patel, A. Kumari, P. K. Singh, and W. C. Hong, “Machine Learning Adoption in Blockchain-Based Smart Applications: The Challenges, and a Way Forward,” IEEE Access, vol. 8, pp. 474–448, 2020, doi: 10.1109/ACCESS.2019.2961372.
  • H. A. Madni, R. M. Umer, and G. L. Foresti, “Blockchain-Based Swarm Learning for the Mitigation of Gradient Leakage in Federated Learning,” IEEE Access, vol. 11, pp. 16549–16556, 2023, doi: 10.1109/ACCESS.2023.3246126.
  • S. Pandey et al., “Do-It-Yourself Recommender System: Reusing and Recycling With Blockchain and Deep Learning,” IEEE Access, vol. 10, pp. 90056–90067, 2022, doi: 10.1109/ACCESS.2022.3199661.
  • H. Kim, S. H. Kim, J. Y. Hwang, and C. Seo, “Efficient privacy-preserving machine learning for blockchain network,” IEEE Access, vol. 7, pp. 136481–136495, 2019, doi: 10.1109/ACCESS.2019.2940052.
  • “Z. Huang, F. Liu, M. Tang, J. Qiu, Y. Peng, “A Distributed Computing Framework Based on Lightweight Variance Reduction Method to Accelerate Machine Learning Training on Blockchain,” China Communications, vol. 17, no. 9, pp. 77-89, Sep. 2020, doi: 10.23919/JCC.2020.09.007”.
  • M. Ghafourian et al., “Combining Blockchain and Biometrics: A Survey on Technical Aspects and a First Legal Analysis,” Feb. 2023, [Online]. Available: http://arxiv.org/abs/2302.10883
  • T. Hewa, M. Ylianttila, and M. Liyanage, “Survey on blockchain based smart contracts: Applications, opportunities and challenges,” Journal of Network and Computer Applications, vol. 177. Academic Press, Mar. 01, 2021. doi: 10.1016/j.jnca.2020.102857.
  • D. Huang, C. J. Chung, Q. Dong, J. Luo, and M. Kang, “Building private blockchains over public blockchains (POP): An attribute-based access control approach,” in Proceedings of the ACM Symposium on Applied Computing, Association for Computing Machinery, 2019, pp. 355–363. doi: 10.1145/3297280.3297317.
  • T. Ncube, N. Dlodlo, and A. Terzoli, “Private Blockchain Networks: A Solution for Data Privacy,” in 2020 2nd International Multidisciplinary Information Technology and Engineering Conference, IMITEC 2020, Institute of Electrical and Electronics Engineers Inc., Nov. 2020. doi: 10.1109/IMITEC50163.2020.9334132.
  • O. Dib, A. Durand, K.-L. Brousmiche, E. Thea, and B. Hamida, “Consortium Blockchains: Overview, Applications and Challenges,” 2018. [Online]. Available: http://www.iariajournals.org/telecommunications/2018,
  • M. Singh, M. A. Rajan, V. L. Shivraj, and P. Balamuralidhar, “Secure MQTT for Internet of Things (IoT),” in Proceedings - 2015 5th International Conference on Communication Systems and Network Technologies, CSNT 2015, Institute of Electrical and Electronics Engineers Inc., Sep. 2015, pp. 746–751. doi: 10.1109/CSNT.2015.16.
  • IEEE Systems Council and Institute of Electrical and Electronics Engineers, ISSE 2017 : 2017 IEEE International Symposium on Systems Engineering : Vienna, Austria, October 11-13, 2017 : 2017 symposium proceedings.
  • F. ARTKIN, “Applications of Artificial Intelligence in Mechanical Engineering,” European Journal of Science and Technology, Dec. 2022, doi: 10.31590/ejosat.1224045.
  • Y. Liu, F. R. Yu, X. Li, H. Ji, and V. C. M. Leung, “Blockchain and Machine Learning for Communications and Networking Systems,” IEEE Communications Surveys and Tutorials, vol. 22, no. 2, pp. 1392–1431, Apr. 2020, doi: 10.1109/COMST.2020.2975911.
  • A. Singh, “Bharati Vidyapeeth’s Institute of Computer Applications and Management (BVICAM),” 2016.
  • S. Naeem, A. Ali, S. Anam, and M. M. Ahmed, “An Unsupervised Machine Learning Algorithms: Comprehensive Review,” International Journal of Computing and Digital Systems, vol. 13, no. 1, pp. 911–921, 2023, doi: 10.12785/ijcds/130172.
  • T. P. Lillicrap et al., “Continuous control with deep reinforcement learning,” Sep. 2015, [Online]. Available: http://arxiv.org/abs/1509.02971
  • A. Alzahrani and T. H. H. Aldhyani, “Artificial Intelligence Algorithms for Detecting and Classifying MQTT Protocol Internet of Things Attacks,” Electronics (Switzerland), vol. 11, no. 22, Nov. 2022, doi: 10.3390/electronics11223837.
  • M. Abdelrazig Abubakar, Z. Jaroucheh, A. Al-Dubai, and X. Liu, “Blockchain-based identity and authentication scheme for MQTT protocol,” in ACM International Conference Proceeding Series, Association for Computing Machinery, Mar. 2021, pp. 73–81. doi: 10.1145/3460537.3460549.
  • F. Buccafurri, V. De Angelis, and R. Nardone, “Securing MQTT by blockchain-based otp authentication,” Sensors (Switzerland), vol. 20, no. 7, Apr. 2020, doi: 10.3390/s20072002.
  • G. Kalele, “An In-Depth Examination of Traditional, Blockchain, and AI-Based Key-Security for The Cyber-Physical IoT Networks,” Institute of Electrical and Electronics Engineers (IEEE), Jul. 2023, pp. 2004–2008. doi: 10.1109/icacite57410.2023.10182722.
  • N. Adhikari and M. Ramkumar, “IoT and Blockchain Integration: Applications, Opportunities, and Challenges,” Network, vol. 3, no. 1, pp. 115–141, Mar. 2023, doi: 10.3390/network3010006.
  • U. Majeed, L. U. Khan, I. Yaqoob, S. M. A. Kazmi, K. Salah, and C. S. Hong, “Blockchain for IoT-based smart cities: Recent advances, requirements, and future challenges,” Journal of Network and Computer Applications, vol. 181. Academic Press, May 01, 2021. doi: 10.1016/j.jnca.2021.103007.
  • S. Menon et al., “Blockchain and Machine Learning Inspired Secure Smart Home Communication Network,” Sensors, vol. 23, no. 13, Jul. 2023, doi: 10.3390/s23136132.
  • K. Nethravathi, A. Tiwari, D. Uike, R. Jaiswal, and K. Pant, “Applications of Artificial Intelligence and Blockchain Technology in Improved Supply Chain Financial Risk Management,” in Proceedings of 5th International Conference on Contemporary Computing and Informatics, IC3I 2022, Institute of Electrical and Electronics Engineers Inc., 2022, pp. 242–246. doi: 10.1109/IC3I56241.2022.10072787.
  • M. Alazab, S. Alhyari, A. Awajan, and A. B. Abdallah, “Blockchain technology in supply chain management: an empirical study of the factors affecting user adoption/acceptance,” Cluster Comput, vol. 24, no. 1, pp. 83–101, Mar. 2021, doi: 10.1007/s10586-020-03200-4.
  • M. Hader, A. Elmhamedi, and A. Abouabdellah, “Blockchain technology in supply chain management and loyalty programs: Toward blockchain implementation in retail market,” in 2020 13th International Colloquium of Logistics and Supply Chain Management, LOGISTIQUA 2020, Institute of Electrical and Electronics Engineers Inc., Dec. 2020. doi: 10.1109/LOGISTIQUA49782.2020.9353879.
  • I. Lahlou and N. Motaki, “Integrating Blockchain with ERP systems for better supply chain performance,” in 2022 IEEE 14th International Conference of Logistics and Supply Chain Management, LOGISTIQUA 2022, Institute of Electrical and Electronics Engineers Inc., 2022. doi: 10.1109/LOGISTIQUA55056.2022.9938086.
  • G. K. Singh and M. Dadhich, “Supply Chain Management Growth With the Adoption of Blockchain Technology (BoT) and Internet of Things (IoT),” Institute of Electrical and Electronics Engineers (IEEE), Jul. 2023, pp. 321–325. doi: 10.1109/icacite57410.2023.10182619.
  • Y. Khaoua, Y. Mouzouna, J. Arif, F. Jawab, and M. Azari, “The Contribution of Blockchain Technology in the Supply Chain Management: The Shipping Industry as an Example,” in 2022 IEEE 14th International Conference of Logistics and Supply Chain Management, LOGISTIQUA 2022, Institute of Electrical and Electronics Engineers Inc., 2022. doi: 10.1109/LOGISTIQUA55056.2022.9938046.
  • M. M. Salim, L. Park, and J. H. Park, “A Machine Learning based Scalable Blockchain architecture for a secure Healthcare system,” in International Conference on ICT Convergence, IEEE Computer Society, 2022, pp. 2231–2234. doi: 10.1109/ICTC55196.2022.9952962.
  • M. J. J. Gul, A. Paul, S. Rho, and M. Kim, “Blockchain based healthcare system with Artificial Intelligence,” in Proceedings - 2020 International Conference on Computational Science and Computational Intelligence, CSCI 2020, Institute of Electrical and Electronics Engineers Inc., Dec. 2020, pp. 740–741. doi: 10.1109/CSCI51800.2020.00138.
  • A. Haddad, M. H. Habaebi, M. R. Islam, and S. A. Zabidi, “Blockchain for Healthcare Medical Records Management System with Sharing Control,” in 2021 IEEE 7th International Conference on Smart Instrumentation, Measurement and Applications, ICSIMA 2021, Institute of Electrical and Electronics Engineers Inc., Aug. 2021, pp. 30–34. doi: 10.1109/ICSIMA50015.2021.9526301.
  • Wajiha and S. R. Patil, “Implementing Blockchain Technology in Healthcare Systems utilizing Machine learning Techniques,” in 2022 IEEE North Karnataka Subsection Flagship International Conference, NKCon 2022, Institute of Electrical and Electronics Engineers Inc., 2022. doi: 10.1109/NKCon56289.2022.10126874.
  • Global IT Research Institute, IEEE Communications Society, and Institute of Electrical and Electronics Engineers, The 23rd International Conference on Advanced Communications Technology : “On-Line security in Pandemic Era!” : Phoenix Park, Pyeongchang, Korea (South), (On-line Conference), Feb. 07-10, 2021 : proceeding & journal.
  • J. Dargan, N. Gupta, and L. Singh, “Blockchain Based Energy Management System: A Proposed Model,” in Proceedings of International Conference on Technological Advancements and Innovations, ICTAI 2021, Institute of Electrical and Electronics Engineers Inc., 2021, pp. 510–514. doi: 10.1109/ICTAI53825.2021.9673233.
  • A. Bin Masood, S. Javaid, Y. Tan, V. Vassiliou, and M. Lestas, “A Blockchain-Based Transactive Energy Management Scheme for Nano-Grids using Power Flow Coloring,” Institute of Electrical and Electronics Engineers (IEEE), Aug. 2023, pp. 187–188. doi: 10.1109/icce-taiwan58799.2023.10226681.
  • A. Jayavarma, Preetha, and M. G. Nair, “A secure energy trading in a smart community by integrating Blockchain and machine learning approach,” Smart Science, 2023, doi: 10.1080/23080477.2023.2270820.
  • F. Mohammadi, M. Sanjari, and M. Saif, “A Real-Time Blockchain-Based State Estimation System for Battery Energy Storage Systems,” in 2022 IEEE Kansas Power and Energy Conference, KPEC 2022, Institute of Electrical and Electronics Engineers Inc., 2022. doi: 10.1109/KPEC54747.2022.9814731.
  • I. Demidov, H. Dibaba, A. Pinomaa, S. Honkapuro, and M. Nieminen, “Energy Management System for Community-Centered Off-Grid System with a Blockchain-Based P2P Energy Market,” in International Conference on the European Energy Market, EEM, IEEE Computer Society, 2023. doi: 10.1109/EEM58374.2023.10161848.
  • T. Cai, J. Wu, C. Yu, and V. Brusic, “Blockchain with Machine Learning for Financial Portfolio Management,” in ICEIEC 2023 - Proceedings of 2023 IEEE 13th International Conference on Electronics, Information and Emergency Communication, Institute of Electrical and Electronics Engineers Inc., 2023, pp. 255–259. doi: 10.1109/ICEIEC58029.2023.10201043.
  • P. Sudha, J. J. Amalraj, and M. Sivakumar, “Lion Swarm Optimization with Deep Learning Driven Predictive Model on Blockchain Financial Product Return Rates,” in Proceedings of the 2023 2nd International Conference on Electronics and Renewable Systems, ICEARS 2023, Institute of Electrical and Electronics Engineers Inc., 2023, pp. 1076–1080. doi: 10.1109/ICEARS56392.2023.10085579.
  • J. Chen, “Research on the Application of Blockchain Technology in Supply Chain Financial Business,” in Proceedings - 2020 2nd International Conference on Applied Machine Learning, ICAML 2020, Institute of Electrical and Electronics Engineers Inc., 2020, pp. 371–374. doi: 10.1109/ICAML51583.2020.00081.
There are 79 citations in total.

Details

Primary Language English
Subjects Computer Software
Journal Section Review
Authors

Maysaa Salama 0009-0001-8381-0492

Early Pub Date September 30, 2025
Publication Date September 30, 2025
Submission Date November 10, 2024
Acceptance Date August 25, 2025
Published in Issue Year 2025 Volume: 8 Issue: 3

Cite

APA Salama, M. (2025). Integrating Blockchain, MQTT, and Machine Learning for Enhanced IoT Applications: A Comprehensive Survey. Sakarya University Journal of Computer and Information Sciences, 8(3), 570-591. https://doi.org/10.35377/saucis...1582663
AMA Salama M. Integrating Blockchain, MQTT, and Machine Learning for Enhanced IoT Applications: A Comprehensive Survey. SAUCIS. September 2025;8(3):570-591. doi:10.35377/saucis.1582663
Chicago Salama, Maysaa. “Integrating Blockchain, MQTT, and Machine Learning for Enhanced IoT Applications: A Comprehensive Survey”. Sakarya University Journal of Computer and Information Sciences 8, no. 3 (September 2025): 570-91. https://doi.org/10.35377/saucis. 1582663.
EndNote Salama M (September 1, 2025) Integrating Blockchain, MQTT, and Machine Learning for Enhanced IoT Applications: A Comprehensive Survey. Sakarya University Journal of Computer and Information Sciences 8 3 570–591.
IEEE M. Salama, “Integrating Blockchain, MQTT, and Machine Learning for Enhanced IoT Applications: A Comprehensive Survey”, SAUCIS, vol. 8, no. 3, pp. 570–591, 2025, doi: 10.35377/saucis...1582663.
ISNAD Salama, Maysaa. “Integrating Blockchain, MQTT, and Machine Learning for Enhanced IoT Applications: A Comprehensive Survey”. Sakarya University Journal of Computer and Information Sciences 8/3 (September2025), 570-591. https://doi.org/10.35377/saucis. 1582663.
JAMA Salama M. Integrating Blockchain, MQTT, and Machine Learning for Enhanced IoT Applications: A Comprehensive Survey. SAUCIS. 2025;8:570–591.
MLA Salama, Maysaa. “Integrating Blockchain, MQTT, and Machine Learning for Enhanced IoT Applications: A Comprehensive Survey”. Sakarya University Journal of Computer and Information Sciences, vol. 8, no. 3, 2025, pp. 570-91, doi:10.35377/saucis. 1582663.
Vancouver Salama M. Integrating Blockchain, MQTT, and Machine Learning for Enhanced IoT Applications: A Comprehensive Survey. SAUCIS. 2025;8(3):570-91.
 Download Cover Image


INDEXING & ABSTRACTING & ARCHIVING


 31045 31044   ResimLink - Resim Yükle  31047 

31043 28939 28938 34240


29070    The papers in this journal are licensed under a Creative Commons Attribution-NonCommercial 4.0 International License