Challenges and Solutions of IoT-Enabled Traffic Surveillance Systems: A Comprehensive Survey

The demand for effective traffic management solutions has increased due to the fast urbanization and rising vehicle density. IoT-enabled traffic surveillance systems have become a viable way to improve road safety, traffic monitoring, and congestion management. Data security, real-time processing constraints, interoperability problems, and high deployment costs are just a few of the major obstacles these systems must overcome. This survey examines various remedies and offers a thorough examination of these issues. We look at important approaches including 5G-enabled communication frameworks, edge computing, blockchain for safe data sharing, and analytics powered by artificial intelligence. We also evaluate new developments and industry best practices that improve the dependability and effectiveness of IoT-based traffic monitoring. Our results emphasize how crucial it is to combine secure communication protocols with sophisticated data processing methods in order to overcome current constraints. The purpose of this study is to help policymakers and researchers create IoT-driven traffic control solutions that are more reliable and scalable.

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Publication Details

Journal
SPU Journal of Science Technology and Management Research
Published
2025-07-01
DOI
https://doi.org/10.63766/spujstmr.24.000042
Primary Topic
Traffic Prediction and Management Techniques
Type
article
Field-Weighted Citation Impact
0.00
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article

Challenges and Solutions of IoT-Enabled Traffic Surveillance Systems: A Comprehensive Survey

Komal Patel, Keta Patel
SPU Journal of Science Technology and Management Research
Traffic Prediction and Management Techniques
article

Challenges and Solutions of IoT-Enabled Traffic Surveillance Systems: A Comprehensive Survey

Komal Patel, Keta Patel
article en

Abstract

The demand for effective traffic management solutions has increased due to the fast urbanization and rising vehicle density. IoT-enabled traffic surveillance systems have become a viable way to improve road safety, traffic monitoring, and congestion management. Data security, real-time processing constraints, interoperability problems, and high deployment costs are just a few of the major obstacles these systems must overcome. This survey examines various remedies and offers a thorough examination of these issues. We look at important approaches including 5G-enabled communication frameworks, edge computing, blockchain for safe data sharing, and analytics powered by artificial intelligence. We also evaluate new developments and industry best practices that improve the dependability and effectiveness of IoT-based traffic monitoring. Our results emphasize how crucial it is to combine secure communication protocols with sophisticated data processing methods in order to overcome current constraints. The purpose of this study is to help policymakers and researchers create IoT-driven traffic control solutions that are more reliable and scalable.

SPU Journal of Science Technology and Management ResearchVol. II(01)
Manubhai Patel Dental College and Hospital (IN)
Openalex Percentile: Top 83%
Traffic Prediction and Management Techniques
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