Vol. 29 No. 2 (2026) Cover Image
Vol. 29 No. 2 (2026)

Published: June 20, 2026

Pages: 301-312

Articles

Towards Next-Generation Autonomous Vehicle Communication in Smart Cities: A Blockchain and AI Perspective

Abstract

Autonomous vehicles (AVs) rely on continuous vehicle-to-vehicle (V2V) and vehicle-to-everything (V2E) communication to support shared perception, traffic direction, and decision making. Integrating real-time data, connectivity, and precise navigation will revolutionize urban mobility by facilitating sustainable, secure, and highly efficient transportation options. The rapid evolution of AVs within smart cities necessitates robust and secure data transmission methods to ensure efficient and safe operations. This paper proposes a next-generation communication framework that integrates blockchain with artificial intelligence (AI) to enable secure, decentralized, and adaptive AVs network. Blockchain is employed as a distributed trust layer to ensure data immutability, decentralized identity management, and transparent transaction validation among vehicles and roadside infrastructure. AI techniques are incorporated to support intelligent threat detection, dynamic resource allocation, anomaly identification, and context-aware decision making. The integration between blockchain and AI enhances trust establishment while maintaining system scalability and real-time responsiveness. The proposed architecture introduces a layered design that separates communication, consensus, and intelligence components, allowing efficient integration with edge computing and 5G-enabled vehicular environments. This research contributes a unified conceptual framework for secure and intelligent autonomous vehicle communication, highlighting how blockchain and AI can jointly address critical limitations in next-generation vehicular networks and support the evolution of trustworthy, resilient, and scalable smart mobility ecosystems.

References

  1. Orieno OH, Ndubuisi NL, Ilojianya VI, Biu PW, Odonkor B. The future of AVs in the US urban landscape: a review: analyzing implications for traffic, urban planning, and the environment. Engineering Science & Technology Journal. 2024 Jan 15;5(1):43-64. https://doi.org/10.51594/estj.v5i1.721
  2. Giannaros A, Karras A, Theodorakopoulos L, Karras C, Kranias P, Schizas N, Kalogeratos G, Tsolis D. AVs: Sophisticated attacks, safety issues, challenges, open topics, blockchain, and future directions. Journal of Cybersecurity and Privacy. 2023 Aug 5;3(3):493-543. https://doi.org/10.3390/jcp3030025
  3. Cai Z, Chen J, Fan Y, Zheng Z, Li K. Blockchain-empowered Federated Learning: Benefits, Challenges, and Solutions. arXiv preprint arXiv:2403.00873. 2024 Mar 1. https://doi.org/10.48550/arXiv.2403.00873
  4. Alam, T. Data Privacy and Security in Autonomous Connected Vehicles in Smart City Environment. Big Data Cogn. Comput. 2024, 8, 95. https://doi.org/10.3390/bdcc8090095
  5. Zhang Q, Wen H, Liu Y, Chang S, Han Z. Federated-reinforcement-learning-enabled joint communication, sensing, and compu-ting resources allocation in connected automated vehicles networks. IEEE Internet of Things Journal. 2022 Jul 4;9(22):23224-40. https://doi.org/10.1109/JIOT.2022.3188434
  6. Alam, T., Gupta, R., Ullah, A., Qamar, S. Blockchain-Enabled Federated Reinforcement Learning (B-FRL) Model for Privacy Preservation Service in IoT Systems. Wireless Pers Commun 136, 2545–2571 (2024). https://doi.org/10.1007/s11277-024-11411-w
  7. Nayak BP, Hota L, Kumar A, Turuk AK, Chong PH. AVs: Resource allocation, security, and data privacy. IEEE Transactions on Green Communications and Networking. 2021 Sep 7;6(1):117-31. https://doi.org/10.1109/TGCN.2021.3110822
  8. Lu Y, Huang X, Zhang K, Maharjan S, Zhang Y. Blockchain empowered asynchronous federated learning for secure data sharing in internet of vehicles. IEEE Transactions on Vehicular Technology. 2020 Feb 13;69(4):4298-311. https://doi.org/10.1109/TVT.2020.2973651
  9. Zhang S, Wang Z, Zhou Z, Wang Y, Zhang H, Zhang G, Ding H, Mumtaz S, Guizani M. Blockchain and federated deep rein-forcement learning based secure cloud-edge-end collaboration in power IoT. IEEE Wireless Communications. 2022 Apr;29(2):84-91. https://doi.org/10.1109/MWC.010.2100491
  10. Qi J, Zhou Q, Lei L, Zheng K. Federated reinforcement learning: Techniques, applications, and open challenges. arXiv preprint arXiv:2108.11887. 2021 Aug 26. https://doi.org/10.20517/ir.2021.02
  11. Lu Y, Huang X, Zhang K, Maharjan S, Zhang Y. Communication-efficient federated learning and permissioned blockchain for digital twin edge networks. IEEE Internet of Things Journal. 2020 Aug 11;8(4):2276-88. https://doi.org/10.1109/JIOT.2020.3015772
  12. Demertzis K. Blockchained federated learning for threat defense. arXiv preprint arXiv:2102.12746. 2021 Feb 25.
  13. Li D, Han D, Weng TH, Zheng Z, Li H, Liu H, Castiglione A, Li KC. Blockchain for federated learning toward secure distributed machine learning systems: a systemic survey. Soft Computing. 2022 May;26(9):4423-40. https://doi.org/10.1007/s00500-021-06496-5
  14. Kumar P, Gupta GP, Tripathi R. TP2SF: A Trustworthy Privacy-Preserving Secured Framework for sustainable smart cities by leveraging blockchain and machine learning. Journal of Systems Architecture. 2021 May 1;115:101954. https://doi.org/10.1016/j.sysarc.2020.101954
  15. Djenouri Y, Michalak TP, Lin JC. Federated deep learning for smart city edge-based applications. Future Generation Computer Systems. 2023 Oct 1;147:350-9. https://doi.org/10.1016/j.future.2023.04.034
  16. Miao Q, Lin H, Wang X, Hassan MM. Federated deep reinforcement learning based secure data sharing for Internet of Things. Computer Networks. 2021 Oct 9;197:108327. https://doi.org/10.1016/j.comnet.2021.108327
  17. Ramu SP, Boopalan P, Pham QV, Maddikunta PK, Huynh-The T, Alazab M, Nguyen TT, Gadekallu TR. Federated learning ena-bled digital twins for smart cities: Concepts, recent advances, and future directions. Sustainable Cities and Society. 2022 Apr 1;79:103663. https://doi.org/10.1016/j.scs.2021.103663
  18. Al-Huthaifi R, Li T, Huang W, Gu J, Li C. Federated learning in smart cities: Privacy and security survey. Information Sciences. 2023 Jun 1;632:833-57. https://doi.org/10.1016/j.ins.2023.03.033
  19. Guo S, Xiang B, Xia X, Yan Z, Li Y. Blockchain and federated learning based data security sharing mechanism over smart city. 2020. https://doi.org/10.21203/rs.3.rs-104012/v1
  20. Li D, Luo Z, Cao B. Blockchain-based federated learning methodologies in smart environments. Cluster Computing. 2022 Aug;25(4):2585-99. https://doi.org/10.1007/s10586-021-03424-y
  21. Moniruzzaman M, Yassine A, Benlamri R. Blockchain and Federated Reinforcement Learning for Vehicle-to-Everything Energy Trading in Smart Grids. IEEE Transactions on Artificial Intelligence. 2023 Mar 29. https://doi.org/10.1109/TAI.2023.3262597
  22. Issa W, Moustafa N, Turnbull B, Sohrabi N, Tari Z. Blockchain-based federated learning for securing internet of things: A com-prehensive survey. ACM Computing Surveys. 2023 Jan 13;55(9):1-43. https://doi.org/10.1145/3560816
  23. Abbas K, Tawalbeh LA, Rafiq A, Muthanna A, Elgendy IA, Abd El-Latif AA. Convergence of blockchain and IoT for secure transportation systems in smart cities. Security and Communication Networks. 2021 Apr 22;2021:1-3. https://doi.org/10.1155/2021/5597679
  24. Choo KK, Gai K, Chiaraviglio L. Blockchain-enabled secure communications in smart cities. Journal of Parallel and Distributed Computing. 2021 Jun 1;152:125-7. https://doi.org/10.1016/j.jpdc.2021.02.021
  25. Pokhrel SR, Choi J. Federated learning with blockchain for AVs: Analysis and design challenges. IEEE Transactions on Commu-nications. 2020 Apr 27;68(8):4734-46. https://doi.org/10.1109/TCOMM.2020.2990686
  26. Qammar A, Karim A, Ning H, Ding J. Securing federated learning with blockchain: a systematic literature review. Artificial Intel-ligence Review. 2023 May;56(5):3951-85. https://doi.org/10.1007/s10462-022-10271-9
  27. Moore E, Imteaj A, Rezapour S, Amini MH. A survey on secure and private federated learning using blockchain: Theory and application in resource-constrained computing. IEEE Internet of Things Journal. 2023 Sep 7. https://doi.org/10.1109/JIOT.2023.3313055
  28. Devarajan GG, Thirunnavukkarasan M, Amanullah SI, Vignesh T, Sivaraman A. An integrated security approach for vehicular networks in smart cities. Transactions on Emerging Telecommunications Technologies. 2023 Nov;34(11):e4757. https://doi.org/10.1002/ett.4757
  29. Sharma PK, Gope P, Puthal D. Blockchain and federated learning-enabled distributed secure and privacy-preserving computing architecture for iot network. In2022 IEEE European Symposium on Security and Privacy Workshops (EuroS&PW) 2022 Jun 6 (pp. 1-9). IEEE. https://doi.org/10.1109/EuroSPW55150.2022.00008
  30. Haddaji A, Ayed S, Chaari L. Federated learning with blockchain approach for trust management in IoV. In International Con-ference on Advanced Information Networking and Applications 2022 Mar 31 (pp. 411-423). Cham: Springer International Pub-lishing. https://doi.org/10.1007/978-3-030-99584-3_36
  31. Qi Y, Hossain MS, Nie J, Li X. Privacy-preserving blockchain-based federated learning for traffic flow prediction. Future Genera-tion Computer Systems. 2021 Apr 1;117:328-37. https://doi.org/10.1016/j.future.2020.12.003
  32. Kakkar R, Gupta R, Agrawal S, Tanwar S, Sharma R. Blockchain-based secure and trusted data sharing scheme for autonomous vehicle underlying 5G. Journal of Information Security and Applications. 2022 Jun 1;67:103179. https://doi.org/10.1016/j.jisa.2022.103179
  33. Otoum S, Al Ridhawi I, Mouftah HT. Blockchain-supported federated learning for trustworthy vehicular networks. InGLOBECOM 2020-2020 IEEE Global Communications Conference 2020 Dec 7 (pp. 1-6). IEEE. https://doi.org/10.1109/GLOBECOM42002.2020.9322159
  34. Ullah I, Deng X, Pei X, Mushtaq H, Uzair M. IoV-SFL: A Blockchain-based Federated Learning Framework for Secure and Efficient Data Sharing in the Internet of Vehicles. Preprint. 2024. https://doi.org/10.21203/rs.3.rs-3648280/v1
  35. Saraswat D, Verma A, Bhattacharya P, Tanwar S, Sharma G, Bokoro PN, Sharma R. Blockchain-based federated learning in UAVs beyond 5G networks: A solution taxonomy and future directions. IEEE Access. 2022 Mar 21;10:33154-82. https://doi.org/10.1109/ACCESS.2022.3161132
  36. Tiba K, Parizi RM, Zhang Q, Dehghantanha A, Karimipour H, Choo KK. Secure blockchain-based traffic load balancing using edge computing and reinforcement learning. Blockchain Cybersecurity, Trust and Privacy. 2020:99-128. https://doi.org/10.1007/978-3-030-38181-3_6
  37. Singh SK, Park L, Park JH. Blockchain-based federated approach for privacy-preserved IoT-enabled smart vehicular networks. In2022 13th International Conference on Information and Communication Technology Convergence (ICTC) 2022 Oct 19 (pp. 1995-1999). IEEE. https://doi.org/10.1109/ICTC55196.2022.9952835
  38. Iordache, Stefan, Catalina Camelia Patilea, and Ciprian Paduraru. "Enhancing Autonomous Vehicle Safety with Blockchain Technology: Securing Vehicle Communication and AI Systems." Future Internet 16.12 (2024): 471. https://doi.org/10.3390/fi16120471
  39. Gebrezgiher, Yonas Teweldemedhin, et al. "Machine learning-based blockchain technology for secure V2X communication: Open challenges and solutions." Sensors 25.15 (2025): 4793. https://doi.org/10.3390/s25154793
  40. Jaiswal, Shalini, and Yaduvir Singh. "Internet of Vehicles for Sustainable Smart Cities: Technologies, Challenges, and Future Perspectives." Driving Innovation at the Intersection of Renewable Energy and the Internet of Vehicles. IGI Global Scientific Publishing, 2025. 35-68. http://doi.org/10.4018/979-8-3373-3321-2.ch002
  41. Heidari, Arash, Seyed Hamed Rastegar, and Ahmad Khonsari. "Artificial Intelligence-driven privacy preservation in the internet of vehicles: a comprehensive systematic literature review." Journal of Big Data (2026). https://doi.org/10.1186/s40537-025-01360-x
  42. Alam, T., Gupta, R., Nasurudeen Ahamed, N., Ullah A., and Almaghthwi A. Smart mobility adoption in sustainable smart cities to establish a growing ecosystem: Challenges and opportunities. MRS Energy & Sustainability (2024). https://doi.org/10.1557/s43581-024-00092-4
  43. Alam, T. Metaverse of Things (MoT) Applications for Revolutionizing Urban Living in Smart Cities. Smart Cities 2024, 7, 2466-2494. https://doi.org/10.3390/smartcities7050096
  44. Patel VA, Bhattacharya P, Tanwar S, Jadav NK, Gupta R. BFLEdge: Blockchain based federated edge learning scheme in V2X underlying 6G communications. In2022 12th international conference on cloud computing, data science & engineering (Conflu-ence) 2022 Jan 27 (pp. 146-152). IEEE. https://doi.org/10.1109/Confluence52989.2022.9734213
  45. Rathee G, Sharma A, Iqbal R, Aloqaily M, Jaglan N, Kumar R. A blockchain framework for securing connected and AVs. Sensors. 2019 Jul 18;19(14):3165. https://doi.org/10.3390/s19143165
  46. Chai H, Leng S, Wu F, He J. Secure and efficient blockchain-based knowledge sharing for intelligent connected vehicles. IEEE Transactions on Intelligent Transportation Systems. 2021 Dec 3;23(9):14620-31. https://doi.org/10.1109/TITS.2021.3131240
  47. Chellapandi VP, Yuan L, Brinton CG, Żak SH, Wang Z. Federated learning for connected and automated vehicles: A survey of existing approaches and challenges. IEEE Transactions on Intelligent Vehicles. 2023 Nov 14. https://doi.org/10.1109/ITSC57777.2023.10421974
  48. Alam, T., Gupta, R., Ahamed, N.N., Ullah A. A decision-making model for self-driving vehicles based on GPT-4V, federated reinforcement learning, and blockchain. Neural Comput & Applic (2024). https://doi.org/10.1007/s00521-024-10161-x
  49. Ahmad J, Zia MU, Naqvi IH, Chattha JN, Butt FA, Huang T, Xiang W. Machine learning and blockchain technologies for cyber-security in connected vehicles. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery. 2024 Jan;14(1):e1515. https://doi.org/10.1002/widm.1515
  50. Behura A, Jain PK, Kumar A. VANETs for Smart Cities. Emerging Electrical and Computer Technologies for Smart Cities: Mod-elling, Solution Techniques and Applications. 2024 Jul 3:69. https://doi.org/10.1201/9781003486930-9
  51. Zhao P, Huang Y, Gao J, Xing L, Wu H, Ma H. Federated learning-based collaborative authentication protocol for shared data in social IoV. IEEE Sensors Journal. 2022 Feb 22;22(7):7385-98. https://doi.org/10.1109/JSEN.2022.3153338
  52. Pinto Neto EC, Sadeghi S, Zhang X, Dadkhah S. Federated reinforcement learning in iot: Applications, opportunities and open challenges. Applied Sciences. 2023 May 26;13(11):6497. https://doi.org/10.3390/app13116497
  53. Liu Y, Yu FR, Li X, Ji H, Leung VC. Blockchain and machine learning for communications and networking systems. ieee com-munications surveys & tutorials. 2020 Feb 24;22(2):1392-431. https://doi.org/10.1109/COMST.2020.2975911
  54. Ogundokun RO, Misra S, Maskeliunas R, Damasevicius R. A review on federated learning and machine learning approaches: Categorization, application areas, and blockchain technology. Information. 2022 May 23;13(5):263. https://doi.org/10.3390/info13050263
  55. Riahi A, Mohamed A, Erbad A. RL-Based Federated Learning Framework Over Blockchain (RL-FL-BC). IEEE Transactions on Network and Service Management. 2023 Feb 1. https://doi.org/10.1109/TNSM.2023.3241437
  56. Sharma A, Podoplelova E, Shapovalov G, Tselykh A, Tselykh A. Sustainable smart cities: convergence of artificial intelligence and blockchain. Sustainability. 2021 Nov 25;13(23):13076. https://doi.org/10.3390/su132313076
  57. Yin X, Qiu H, Wu X, Zhang X. An Efficient Attribute-Based Participant Selecting Scheme with Blockchain for Federated Learning in Smart Cities. Computers. 2024 May 9;13(5):118. https://doi.org/10.3390/computers13050118
  58. Zheng Z, Zhou Y, Sun Y, Wang Z, Liu B, Li K. Applications of federated learning in smart cities: recent advances, taxonomy, and open challenges. Connection Science. 2022 Dec 31;34(1):1-28. https://doi.org/10.1080/09540091.2021.1936455
  59. Alam, Tanweer. "Breaking the Traffic Code: How MaaS Is Shaping Sustainable Mobility Ecosystems." Future Transportation 5.3 (2025): 94. https://doi.org/10.3390/futuretransp5030094
  60. Liu J, Chen C, Li Y, Sun L, Song Y, Zhou J, Jing B, Dou D. Enhancing trust and privacy in distributed networks: a comprehensive survey on blockchain-based federated learning. Knowledge and Information Systems. 2024 Apr 25:1-27. https://doi.org/10.1007/s10115-024-02117-3
  61. Malik JA. Next-Generation Protection: Leveraging Federated Learning and Blockchain for Intrusion Detection in Smart Vehicle Network. Power System Technology. 2024 May 3;48(1):931-52. https://doi.org/10.52783/pst.353
  62. Dhasaratha C, Hasan MK, Islam S, Khapre S, Abdullah S, Ghazal TM, Alzahrani AI, Alalwan N, Vo N, Akhtaruzzaman M. Data privacy model using blockchain reinforcement federated learning approach for scalable internet of medical things. CAAI Trans-actions on Intelligence Technology. 2024 Feb 6. https://doi.org/10.1049/cit2.12287
  63. Sameera KM, Nicolazzo S, Arazzi M, Nocera A, KA RR, Vinod P, Conti M. Privacy-preserving in Blockchain-based Federated Learning systems. Computer Communications. 2024 Apr 20. https://doi.org/10.48550/arXiv.2401.03552