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SVM-Based Traffic Data Classification for Secured IoT-Based Road Signaling System

SVM-Based Traffic Data Classification for Secured IoT-Based Road Signaling System

Suresh Sankaranarayanan, Srijanee Mookherji
Copyright: © 2019 |Volume: 15 |Issue: 1 |Pages: 29
ISSN: 1548-3657|EISSN: 1548-3665|EISBN13: 9781522564324|DOI: 10.4018/IJIIT.2019010102
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MLA

Sankaranarayanan, Suresh, and Srijanee Mookherji. "SVM-Based Traffic Data Classification for Secured IoT-Based Road Signaling System." IJIIT vol.15, no.1 2019: pp.22-50. http://doi.org/10.4018/IJIIT.2019010102

APA

Sankaranarayanan, S. & Mookherji, S. (2019). SVM-Based Traffic Data Classification for Secured IoT-Based Road Signaling System. International Journal of Intelligent Information Technologies (IJIIT), 15(1), 22-50. http://doi.org/10.4018/IJIIT.2019010102

Chicago

Sankaranarayanan, Suresh, and Srijanee Mookherji. "SVM-Based Traffic Data Classification for Secured IoT-Based Road Signaling System," International Journal of Intelligent Information Technologies (IJIIT) 15, no.1: 22-50. http://doi.org/10.4018/IJIIT.2019010102

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Abstract

The traffic controlling systems at present are microcontroller-based, which is semi-automatic in nature where time is the only parameter that is considered. With the introduction of IoT in traffic signaling systems, research is being done considering density as a parameter for automating the traffic signaling system and regulate traffic dynamically. Security is a concern when sensitive data of great volume is being transmitted wirelessly. Security protocols that have been implemented for IoT networks can protect the system against attacks and are purely based on standard cryptosystem. They cannot handle heterogeneous data type. To prevent the issues on security protocols, the authors have implemented SVM machine learning algorithm for analyzing the traffic data pattern and detect anomalies. The SVM implementation has been done for the UK traffic data set between 2011-2016 for three cities. The implementation been carried out in Raspberry Pi3 processor functioning as an edge router and SVM machine learning algorithm using Python Scikit Libraries.

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