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Explainable Artificial Intelligence and Fuzzy MCDM Approach in Location Selection of Roadside Units in Vehicular Networks With Economic Efficiency
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Author(s): A. P. Pushpalatha (Velammal College of Engineering and Technology, India), Utpal Saikia (Silapathar College, India), Y. Monikarchana (Mohan Babu University, India), Sakshi Taaresh Khanna (Ram Lal Anand College, New Delhi, India), Sulbha Raorane (St. Francis Institute of Management and Research, India)and Chitra Ramaprakash (Dayananda Sagar College of Engineering, India)
Copyright: 2025
Pages: 20
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.ch002
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Abstract
Decision-making on the selection-based problem of the location of Roadside units (RSU) in vehicular networks is complicated. This intricate problem shall be resolved with the intervention of Explainable Artificial Intelligence (XAI) and fuzzy-based multi-criteria decision-making methods (MCDM). This research proposes the integrated decision-making approach in making optimal location selection considering different criteria and economic aspects. The decision-making problem discussed in this chapter considers location selection of roadside units as the choice-making of the location is very significant in enhancing vehicular efficiency. This decision problem considers the criteria of coverage area, traffic density, accident frequency, infrastructure availability, environmental impact, energy efficiency, and cost components such as cost effectiveness, revenue competence, and investment returns. The integrated approach is leveraged to make an optimal choice of the RSU locations.
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