Alhamdulillah! I am delighted to announce that our paper has been accepted in Future Generation Computer Systems (Elsevier, Q1 Journal, Impact Factor: 6.1)!
Accepted Paper
Title: "Quality of Experience Aware Task Execution in Digital Twinning Vehicular Edge Computing: A Framework and A3C Algorithm"
Journal: Future Generation Computer Systems (Elsevier) • Q1 • Impact Factor: 6.1
Authors: Mostakim Jihad, Abdullah Al Fahad, Palash Roy, Md. Abdur Razzaque, Abdulhameed Alelaiwi, Md. Rafiul Hassan, and M. Mehedi Hassan
View Paper on ScienceDirectThis journey was not easy. We faced tough reviews from four reviewers, and finally, after resubmission yesterday, received the acceptance today! The rigorous peer-review process challenged us to strengthen every aspect of our work, and the final result reflects that collective effort.
Acknowledgments
I cannot express enough gratitude to my supervisor, Palash Roy sir, for his unimaginable effort, countless nights working with me, kind supervision, and always pushing me toward better. This achievement would not have been possible without your tireless support.
Also, a big thanks to my co-author Abdullah Al Fahad — your hard work, collaboration, and dedication were invaluable throughout this process.
At the same time, I am deeply thankful to Md. Abdur Razzaque sir for his invaluable guidance, sharp insights, and constant encouragement throughout this journey. I truly feel lucky to have had the opportunity to work with him and to be guided with such kindness.
This acceptance is a motivation to continue contributing to more research work and exploring new challenges in this field. The journey of research is demanding but deeply rewarding — and this milestone only strengthens my resolve to push further.
Research Context
This paper proposes a novel framework for task execution in Digital Twinning Vehicular Edge Computing (DTVEC) environments, leveraging the Advantage Actor-Critic (A3C) reinforcement learning algorithm to optimize Quality of Experience (QoE) for vehicular applications. The work addresses critical challenges in resource allocation and task scheduling at the network edge for next-generation intelligent transportation systems.