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DT-VAR: Decision Tree Predicted Compatibility-Based Vehicular Ad-Hoc Reliable Routing

Authors
Kumbhar, Farooque HassanShin, Soo Young
Issue Date
Jan-2021
Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Keywords
Ad-hoc routing; decision tree; machine learning; reliable routing; VANET
Citation
IEEE WIRELESS COMMUNICATIONS LETTERS, v.10, no.1, pp.87 - 91
Journal Title
IEEE WIRELESS COMMUNICATIONS LETTERS
Volume
10
Number
1
Start Page
87
End Page
91
URI
https://scholarworks.bwise.kr/kumoh/handle/2020.sw.kumoh/18564
DOI
10.1109/LWC.2020.3021430
ISSN
2162-2337
Abstract
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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