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Machine Learning for Optimal Matching Management Techniques With Vehicular Network Communication Models
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Author(s): N. Anitha (Periyar University Centre for Postgraduate and Research Studies, India), A. Manshath (B.S. Abdur Rahman Institute of Science and Technology, India), Shaik Moinuddin Imran (Sri Venkateswara Institute of Technology, India), B. Ranjitha (Mohan Babu University, India), Manish Kumar Thakur (Acharya Institute of Technology, India)and Mohit Tiwari (Bharat Vidyapeeth's College of Engineering, India)
Copyright: 2025
Pages: 18
Source title:
Networking, Transport, and Quality of Service in Vehicular Networks
Source Author(s)/Editor(s): Truong Khang Nguyen (Van Lang University, Vietnam)and Devasis Pradhan (Acharya Institute of Technology, India)
DOI: 10.4018/979-8-3693-6422-2.ch004
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Abstract
The varied types of Vehicular Networks Communication models (VNCM) circumscribe the decision-makers into a chaotic decision-making environment. The choice-making of VNCM is purely dependent on the attributes of traffic volume, vehicle density, road type, weather conditions, and application demands. However, the attribute of management techniques with the attribute values of dynamic routing, load balancing, and congestion control is more significant in choosing the desirable communication model. This challenging decision-making problem shall be resolved by applying the machine learning approach. This chapter proposes a matching model with supervised data that associates VNCM with management techniques and other core attributes. Supervised machine learning algorithms are applied to develop a matching model that best associates the VNCM with the management techniques considering vehicular efficiency. The performance metrics are computed to determine the most promising algorithm and to validate the consistency of the model. The results of the decision model facilitate the choice-making of the VNCM and also serve as the foundation for the construction of a predictive model.
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