@article{4783,
  author = {Dit Suthiwong},
  title = {Faculty of Information Technology, King Mongkut's University of Technology North Bangkok, Bangkok, Thailand},
  journal = {Journal of Networking Technology},
  year = {2026},
  volume = {17},
  number = {3},
  doi = {https://doi.org/10.6025/jnt/2026/17/3/136-160},
  url = {https://www.dline.info/jnt/fulltext/v17n3/jntv17n3_2.pdf},
  abstract = {The evolution of network management toward user centric Quality of Experience (QoE) necessitates intelligent
routing frameworks capable of balancing multimedia quality, communication reliability, and energy
efficiency in dynamic environments such as fifth generation Vehicular Ad Hoc Networks (5G VANETs). This
study presents an explainable machine learning approach to analyze and predict QoE-aware energy-efficient
routing decisions using a simulated dataset of 10,000 routing instances encompassing network, multimedia,
and energy related parameters. A Random Forest classifier with balanced class weighting was employed,
achieving near perfect predictive performance with 99.9% accuracy, 99.56% balanced accuracy, and a
Matthews Correlation Coefficient of 0.998. To address the "black box" nature of machine learning, SHapley
Additive exPlanations (SHAP) provided both global and local interpretability, consistently identifying packet
delay and video quality (PSNR) as the dominant determinants of routing outcomes. High_QoE_Route instances
exhibited superior performance, characterized by lower delays (32.94 ms) and higher PSNR (43.96 dB),
while maintaining competitive energy consumption profiles. Statistical analysis confirmed significant
differences across routing classes for delay and PSNR. The findings demonstrate that effective routing emerges
from optimizing multiple performance dimensions simultaneously, where delay and perceptual quality
outweigh packet loss in influencing user satisfaction. By integrating high predictive accuracy with model
transparency, the proposed framework enhances trustworthiness and practical deployability, contributing
toward more reliable, sustainable, and intelligent vehicular communication systems for next generation
transportation infrastructures.},
}