International Journal of

ADVANCED AND APPLIED SCIENCES

EISSN: 2313-3724, Print ISSN: 2313-626X

Frequency: 12
line decor
  
line decor

 Volume 11, Issue 7 (July 2024), Pages: 1-10

----------------------------------------------

 Original Research Paper

Secure and transparent traffic congestion control system for smart city using a federated learning approach

 Author(s): 

 Muhammad Hassan Ghulam Muhammad 1, Reyaz Ahmad 2, Areej Fatima 3, Abdul Salam Mohammed 2, Muhammad Ahsan Raza 4, Muhammad Adnan Khan 5, *

 Affiliation(s):

 1Department of Computer Science, National College of Business Administration and Economics, Lahore, Pakistan
 2Department of General Education, Skyline University College, University City Sharjah, 1797, Sharjah, United Arab Emirates
 3Department of Computer Science, Lahore Garrison University, Lahore, Pakistan
 4Department of Information Sciences, University of Education, Multan Campus 60000, Lahore, Pakistan
 5Faculty of Artificial Intelligence and Software, Department of Software, Gachon University, Seongnam, Gyeonggi-do, 13120, South Korea

 Full text

  Full Text - PDF

 * Corresponding Author. 

  Corresponding author's ORCID profile: https://orcid.org/0000-0003-4854-9935

 Digital Object Identifier (DOI)

 https://doi.org/10.21833/ijaas.2024.07.001

 Abstract

This study addresses the increasing problems of traffic congestion in smart cities by introducing a Secure and Transparent Traffic Congestion Control System using federated learning. Traffic congestion control systems face key issues such as data privacy, security vulnerabilities, and the necessity for joint decision-making. Federated learning, a type of distributed machine learning, is effective because it allows for training models on decentralized data while maintaining data privacy. Furthermore, incorporating blockchain technology improves the system’s security, integrity, and transparency. The proposed system uses federated learning to securely gather and analyze local traffic data from different sources within a smart city without moving sensitive data away from its original location. This method minimizes the risk of data breaches and privacy issues. Blockchain technology creates a permanent, transparent record for monitoring and confirming decisions related to traffic congestion control, thereby promoting trust and accountability. The combination of federated learning's decentralized nature and blockchain's secure, transparent features aids in building a strong traffic management system for smart cities. This research contributes to advancements in smart city technology, potentially improving traffic management and urban living standards. Moreover, tests of the new combined model show a high accuracy rate of 97.78% and a low miss rate of 2.22%, surpassing previous methods. The demonstrated efficiency and adaptability of the model to various smart city environments and its scalability in expanding urban areas are crucial for validating its practical use in real-world settings.

 © 2024 The Authors. Published by IASE.

 This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

 Keywords

 Federated learning, Blockchain technology, Traffic congestion control, Smart cities, Data privacy

 Article history

 Received 28 October 2023, Received in revised form 8 June 2024, Accepted 14 June 2024

 Acknowledgment 

No Acknowledgment.

 Compliance with ethical standards

 Conflict of interest: The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

 Citation:

 Muhammad MHG, Ahmad R, Fatima A, Mohammed AS, Raza MA, and Khan MA (2024). Secure and transparent traffic congestion control system for smart city using a federated learning approach. International Journal of Advanced and Applied Sciences, 11(7): 1-10

 Permanent Link to this page

 Figures

 Fig. 1 Fig. 2

 Tables

 Table 1 Table 2 Table 3 Table 4 Table 5 Table 6 

----------------------------------------------   

 References (30)

  1. Al-Sakran HO (2015). Intelligent traffic information system based on integration of Internet of Things and agent technology. International Journal of Advanced Computer Science and Applications, 6(2): 37-43. https://doi.org/10.14569/IJACSA.2015.060206   [Google Scholar]
  2. Arel I, Liu C, Urbanik T, and Kohls AG (2010). Reinforcement learning-based multi-agent system for network traffic signal control. IET Intelligent Transport Systems, 4(2): 128-135. https://doi.org/10.1049/iet-its.2009.0070   [Google Scholar]
  3. Awan FM, Saleem Y, Minerva R, and Crespi N (2020). A comparative analysis of machine/deep learning models for parking space availability prediction. Sensors, 20(1): 322. https://doi.org/10.3390/s20010322   [Google Scholar] PMid:31935953 PMCid:PMC6983166
  4. Bui KH N, Camacho D, and Jung JE (2017). Real-time traffic flow management based on inter-object communication: A case study at intersection. Mobile Networks and Applications, 22: 613-624. https://doi.org/10.1007/s11036-016-0800-y   [Google Scholar]
  5. Cao X, Zhang J, and Poor HV (2018). An optimal auction mechanism for mobile edge caching. In the 38th International Conference on Distributed Computing Systems, IEEE, Vienna, Austria: 388-399. https://doi.org/10.1109/ICDCS.2018.00046   [Google Scholar]
  6. Chakraborty P, Adu-Gyamfi YO, Poddar S, Ahsani V, Sharma A, and Sarkar S (2018). Traffic congestion detection from camera images using deep convolution neural networks. Transportation Research Record, 2672(45): 222-231. https://doi.org/10.1177/0361198118777631   [Google Scholar]
  7. Djahel S, Salehie M, Tal I, and Jamshidi P (2013). Adaptive traffic management for secure and efficient emergency services in smart cities. In the International Conference on Pervasive Computing and Communications Workshops, IEEE, San Diego, USA: 340-343. https://doi.org/10.1109/PerComW.2013.6529511   [Google Scholar]
  8. Fadlullah ZM, Tang F, Mao B, Kato N, Akashi O, Inoue T, and Mizutani K (2017). State-of-the-art deep learning: Evolving machine intelligence toward tomorrow’s intelligent network traffic control systems. IEEE Communications Surveys and Tutorials, 19(4): 2432-2455. https://doi.org/10.1109/COMST.2017.2707140   [Google Scholar]
  9. Fatima A, Khan MA, Abbas S, Waqas M, Anum L, and Asif M (2019). Evaluation of planet factors of smart city through multi-layer fuzzy logic (MFL). The ISC International Journal of Information Security (ISeCure), 11(3): 51-58. https://doi.org/10.22042/isecure.2019.11.0.7   [Google Scholar]
  10. Guo M, Wang P, Chan CY, and Askary S (2019). A reinforcement learning approach for intelligent traffic signal control at urban intersections. In the Intelligent Transportation Systems Conference, IEEE, Auckland, New Zealand: 4242-4247. https://doi.org/10.1109/ITSC.2019.8917268   [Google Scholar] PMid:30900705
  11. Huang W, Song G, Hong H, and Xie K (2014). Deep architecture for traffic flow prediction: Deep belief networks with multitask learning. IEEE Transactions on Intelligent Transportation Systems, 15(5): 2191-2201. https://doi.org/10.1109/TITS.2014.2311123   [Google Scholar]
  12. Huang Y, Song X, Ye F, Yang Y, and Li X (2017). Fair caching algorithms for peer data sharing in pervasive edge computing environments. In the 37th International Conference on Distributed Computing Systems, IEEE, Atlanta, USA: 605-614. https://doi.org/10.1109/ICDCS.2017.151   [Google Scholar]
  13. Ibrahim M, Abbas S, Fatima A, Ghazal TM, Saleem M, Alharbi M, and Elmitwally N (2024). Fuzzy-based fusion model for β-thalassemia carriers prediction using machine learning technique. Advances in Fuzzy Systems, 2024: 4468842. https://doi.org/10.1155/2024/4468842   [Google Scholar]
  14. Jadhav P, Kelkar P, Patil K, and Thorat S (2016). Smart traffic control system using image processing. International Research Journal of Engineering and Technology, 3(3): 2395-0056.   [Google Scholar]
  15. Jain K, Choudhury T, and Kashyap N (2017). Smart vehicle identification system using OCR. In the 3rd International Conference on Computational Intelligence and Communication Technology, IEEE, Ghaziabad, India: 1-6. https://doi.org/10.1109/CIACT.2017.7977297   [Google Scholar]
  16. Jiang P, Liu Z, Zhang L, and Wang J (2022). Advanced traffic congestion early warning system based on traffic flow forecasting and extenics evaluation. Applied Soft Computing, 118: 108544. https://doi.org/10.1016/j.asoc.2022.108544   [Google Scholar]
  17. Kabir MH, Hasan KF, Hasan MK, and Ansari K (2022). Explainable artificial intelligence for smart city application: A secure and trusted platform. In: Ahmed M, Islam SR, Anwar A, Moustafa N, and Pathan ASK (Eds.), Explainable artificial intelligence for cyber security: Next generation artificial intelligence: 241-263. Springer International Publishing, Cham, Switzerland. https://doi.org/10.1007/978-3-030-96630-0_11   [Google Scholar]
  18. Khan AH, Khan MA, Abbas S, Siddiqui SY, Saeed MA, Alfayad M, and Elmitwally NS (2021). Simulation, modeling, and optimization of intelligent kidney disease predication empowered with computational intelligence approaches. Computers, Materials and Continua, 67(2): 1399-1412. https://doi.org/10.32604/cmc.2021.012737   [Google Scholar]
  19. Lee HJ, Ullah I, Wan W, Gao Y, and Fang Z (2019). Real-time vehicle make and model recognition with the residual SqueezeNet architecture. Sensors, 19(5): 982. https://doi.org/10.3390/s19050982   [Google Scholar] PMid:30813512 PMCid:PMC6427723
  20. Li L, Lv Y, and Wang FY (2016). Traffic signal timing via deep reinforcement learning. IEEE/CAA Journal of Automatica Sinica, 3(3): 247-254. https://doi.org/10.1109/JAS.2016.7508798   [Google Scholar]
  21. Liu Y, Lyu C, Liu Z, and Cao J (2021). Exploring a large-scale multi-modal transportation recommendation system. Transportation Research Part C: Emerging Technologies, 126: 103070. https://doi.org/10.1016/j.trc.2021.103070   [Google Scholar]
  22. Malik JA and Saleem M (2023). Blockchain and cyber-physical system for security engineering in the smart industry. In: Motahhir S and Maleh Y (Eds.), Security engineering for embedded and cyber-physical systems: 51-70. 1st Edition, CRC Press, Boca Raton, USA.   [Google Scholar]
  23. Osman T, Psyche SS, Ferdous JS, and Zaman HU (2017). Intelligent traffic management system for cross section of roads using computer vision. In the 7th Annual Computing and Communication Workshop and Conference, IEEE, Las Vegas, USA: 1-7. https://doi.org/10.1109/CCWC.2017.7868350   [Google Scholar]
  24. Saleem M, Khan MS, Issa GF, Khadim A, Asif M, Akram AS, and Nair HK (2023). Smart spaces: Occupancy detection using adaptive back-propagation neural network. In the International Conference on Business Analytics for Technology and Security, IEEE, Dubai, UAE: 1-6. https://doi.org/10.1109/ICBATS57792.2023.10111286   [Google Scholar]
  25. Shekar BS, Divyashree C, George G, Rani HU, Murali A, and Kumar GN (2012). GPS based shortest path for ambulances using VANETs. In the International Conference on Wireless Networks (ICWN 2012), IACSIT Press, Singapore, Singapore, 49: 190-196.   [Google Scholar]
  26. Sivaraman S and Trivedi MM (2010). A general active-learning framework for on-road vehicle recognition and tracking. IEEE Transactions on Intelligent Transportation Systems, 11(2): 267-276. https://doi.org/10.1109/TITS.2010.2040177   [Google Scholar]
  27. Sundar R, Hebbar S, and Golla V (2014). Implementing intelligent traffic control system for congestion control, ambulance clearance, and stolen vehicle detection. IEEE Sensors Journal, 15(2): 1109-1113. https://doi.org/10.1109/JSEN.2014.2360288   [Google Scholar]
  28. Swathi K, Sivanagaraju V, Manikanta AKS, and Kumar SD (2016). Traffic density control and accident indicator using WSN. Traffic, 2(4): 18-21.   [Google Scholar]
  29. Wu G, Chen J, Bao W, Zhu X, Xiao W, Wang J, and Liu L (2017). MECCAS: Collaborative storage algorithm based on alternating direction method of multipliers on mobile edge cloud. In the International Conference on Edge Computing, IEEE, Honolulu, USA: 40-46. https://doi.org/10.1109/IEEE.EDGE.2017.14   [Google Scholar]
  30. Yaga D, Mell P, Roby N, and Scarfone K (2018). Blockchain technology overview. NISTIR 8202, National Institute of Standards and Technology, Gaithersburg, USA. https://doi.org/10.6028/NIST.IR.8202   [Google Scholar]