Journal
IEEE WIRELESS COMMUNICATIONS LETTERS
Volume 10, Issue 1, Pages 87-91Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/LWC.2020.3021430
Keywords
Ad-hoc routing; decision tree; machine learning; reliable routing; VANET
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Funding
- Korea Research Fellowship Program through the National Research Foundation of Korea - Ministry of Science and ICT [2019H1D3A1A01102978]
- National Research Foundation of Korea [2019H1D3A1A01102978] Funding Source: Korea Institute of Science & Technology Information (KISTI), National Science & Technology Information Service (NTIS)
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The study investigates the use of machine learning-based classifications in VANETs for improved routing and message delivery efficiency. By leveraging a fog node architecture, higher packet delivery ratio and longer connectivity were achieved.
Reliable routing and efficient message delivery in vehicular ad-hoc networks (VANETs) is a significant challenge owing to underlying environment constraints, such as dynamic nature, mobility, and limited connectivity. With the increasing number of machine learning (ML) applications in wireless networks, VANETs can benefit from these data-driven predictions. In this letter, we innovate and investigate ML-based classifications in VANETs to predict the most suitable path with the longest compatibility time and trust using a fog node based VANET architecture. The proposed scheme in SUMO VANET traces achieves up to a 16% packet delivery ratio (PDR) with a 99% accuracy and longer connectivity with only 3 similar to 4 hops, compared with existing AOMDV and TCSR solutions with merely a 4% PDR.
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