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

被引:0
作者
Muhammad, Muhammad Hassan Ghulam [1 ]
Ahmad, Reyaz [2 ]
Fatima, Areej [3 ]
Mohammed, Abdul Salam [2 ]
Raza, Muhammad Ahsan [4 ]
Khan, Muhammad Adnan [5 ]
机构
[1] Natl Coll Business Adm & Econ, Dept Comp Sci, Lahore, Pakistan
[2] Skyline Univ Coll, Dept Gen Educ, Univ City Sharjah, Sharjah 1797, U Arab Emirates
[3] Lahore Garrison Univ, Dept Comp Sci, Lahore, Pakistan
[4] Univ Educ, Dept Informat Sci, Multan Campus, Lahore 60000, Pakistan
[5] Gachon Univ, Fac Artificial Intelligence & Software, Dept Software, Seongnam 13120, Gyeonggi Do, South Korea
来源
INTERNATIONAL JOURNAL OF ADVANCED AND APPLIED SCIENCES | 2024年 / 11卷 / 07期
关键词
Federated learning; Blockchain technology; Traffic congestion control; Smart cities; Data privacy;
D O I
10.21833/ijaas.2024.07.001
中图分类号
O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];
学科分类号
07 ; 0710 ; 09 ;
摘要
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. cy. 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. (c) 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/).
引用
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页码:1 / 10
页数:10
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