Smart City Gnosys
Smart city article details
| Title | Cybersecurity In A Scalable Smart City Framework Using Blockchain And Federated Learning For Internet Of Things (Iot) |
|---|---|
| ID_Doc | 17023 |
| Authors | Sefati S.S.; Craciunescu R.; Arasteh B.; Halunga S.; Fratu O.; Tal I. |
| Year | 2024 |
| Published | Smart Cities, 7, 5 |
| DOI | http://dx.doi.org/10.3390/smartcities7050109 |
| Abstract | Highlights What are the main findings? Implementation of blockchain enhances the security and scalability of smart city frameworks. Federated Learning enables efficient and privacy-preserving data sharing among IoT devices. What are the implications of the main finding? The proposed framework significantly reduces the risk of data breaches in smart city infrastructures. Improved data privacy and security can foster greater adoption of IoT technologies in urban environments.Highlights What are the main findings? Implementation of blockchain enhances the security and scalability of smart city frameworks. Federated Learning enables efficient and privacy-preserving data sharing among IoT devices. What are the implications of the main finding? The proposed framework significantly reduces the risk of data breaches in smart city infrastructures. Improved data privacy and security can foster greater adoption of IoT technologies in urban environments.Abstract Smart cities increasingly rely on the Internet of Things (IoT) to enhance infrastructure and public services. However, many existing IoT frameworks face challenges related to security, privacy, scalability, efficiency, and low latency. This paper introduces the Blockchain and Federated Learning for IoT (BFLIoT) framework as a solution to these issues. In the proposed method, the framework first collects real-time data, such as traffic flow and environmental conditions, then normalizes, encrypts, and securely stores it on a blockchain to ensure tamper-proof data management. In the second phase, the Data Authorization Center (DAC) uses advanced cryptographic techniques to manage secure data access and control through key generation. Additionally, edge computing devices process data locally, reducing the load on central servers, while federated learning enables distributed model training, ensuring data privacy. This approach provides a scalable, secure, efficient, and low-latency solution for IoT applications in smart cities. A comprehensive security proof demonstrates BFLIoT's resilience against advanced cyber threats, while performance simulations validate its effectiveness, showing significant improvements in throughput, reliability, energy efficiency, and reduced delay for smart city applications. |
| Author Keywords | blockchain; data privacy; federated learning; IoT security; smart cities |
Similar Articles
| Id | Similarity | Authors | Title | Published |
|---|---|---|---|---|
5652  | 0.941 | Wang S.; Chen C.; Han B.; Zhu J. | A Trusted And Decentralized Federated Learning Framework For Iot Devices In Smart City | Proceedings - IEEE Congress on Cybermatics: 2024 IEEE International Conferences on Internet of Things, iThings 2024, IEEE Green Computing and Communications, GreenCom 2024, IEEE Cyber, Physical and Social Computing, CPSCom 2024, IEEE Smart Data, SmartData 2024 (2024) |
| 3195 | 0.931 | Hai T.; Wang D.; Seetharaman T.; Amelesh M.; Sreejith P.M.; Sharma V.; Ibeke E.; Liu H. | A Novel & Innovative Blockchain-Empowered Federated Learning Approach For Secure Data Sharing In Smart City Applications | Lecture Notes in Networks and Systems, 735 LNNS (2023) |
| 496 | 0.922 | Bekkali A.E.; Essaaidi M.; Boulmalf M. | A Blockchain-Based Architecture And Framework For Cybersecure Smart Cities | IEEE Access, 11 (2023) |
| 12359 | 0.914 | Sharma V.; Seetharaman T.; Bd V.; Khangaonkar A.M. | Blockchain And Federated Learning Enabled Smart Traffic Management System For Smart Cities | 4th International Conference on Intelligent Engineering and Management, ICIEM 2023 (2023) |
| 17680 | 0.914 | Islam R.; Bose R.; Roy S.; Khan A.A.; Sutradhar S.; Das S.; Ali F.; AlZubi A.A. | Decentralized Trust Framework For Smart Cities: A Blockchain-Enabled Cybersecurity And Data Integrity Model | Scientific Reports, 15, 1 (2025) |
| 47807 | 0.913 | Shankar A.; Maple C. | Securing The Internet Of Things-Enabled Smart City Infrastructure Using A Hybrid Framework | Computer Communications, 205 (2023) |
| 35041 | 0.913 | Moawad M.M.; Madbouly M.M.; Guirguis S.K. | Leveraging Blockchain And Machine Learning To Improve Iot Security For Smart Cities | Lecture Notes on Data Engineering and Communications Technologies, 164 (2023) |
| 35956 | 0.909 | Dritsas E.; Trigka M. | Machine Learning For Blockchain And Iot Systems In Smart Cities: A Survey | Future Internet, 16, 9 (2024) |
| 12664 | 0.908 | Anitha A.; Haritha T. | Blockchain-Based Shielding Framework To Enhance Security In Smart Cities | Artifcial Intelligence and IoT for Cyber Security Solutions in Smart Cities (2025) |
| 22956 | 0.906 | Djenouri Y.; Belbachir A.N. | Empowering Urban Connectivity In Smart Cities Using Federated Intrusion Detection | 2023 IEEE 10th International Conference on Data Science and Advanced Analytics, DSAA 2023 - Proceedings (2023) |