Deep Reinforcement-Driven Clustering and Routing Protocol for Smart Vehicular Networks

Riki, Riki and Setyawan, Widyarto Deep Reinforcement-Driven Clustering and Routing Protocol for Smart Vehicular Networks. The International Journal of Artificial Intelligence Research, 9 (2). ISSN 2579-7298

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Official URL: https://ijair.id/index.php/ijair/index

Abstract

Abstract This study proposes a Deep Reinforcement-Driven Clustering and Routing Protocol (DRCRP) to enhance energy efficiency and routing stability in smart vehicular networks. The protocol integrates an Actor–Critic deep reinforcement learning framework with Proximal Policy Optimization (PPO) to enable adaptive decision-making in dynamic Internet of Vehicles (IoV) environments. Through continuous learning, DRCRP adjusts cluster head selection and routing paths according to real-time vehicular mobility, residual energy, and link quality. Simulation experiments conducted using NS-2 and VanetMobiSim show that DRCRP achieves superior performance compared to benchmark algorithms such as AI-EECR, GWO-CH, and DMCNF. Quantitatively, the proposed model improved the Packet Delivery Ratio (PDR) by up to 4.3%, reduced End-to-End Delay by 18–22%, and lowered Energy Consumption by 12–16%. Moreover, DRCRP effectively minimized communication overhead and extended cluster head and member lifetimes, confirming its ability to balance reliability and energy efficiency. These results demonstrate the capability of reinforcement learning-based architectures to support intelligent, sustainable, and scalable vehicular communication systems under complex mobility conditions

Item Type:	Article
Subjects:	000 Karya Umum > 006 Metode Komputer Tertentu > 006.242 Kode Bar > 006.3 Kecerdasan Buatan 000 Karya Umum > 006 Metode Komputer Tertentu > 006.3 Kecerdasan Buatan
Divisions:	Fakultas Sains & Teknologi > Sistem Informasi
Depositing User:	Riki Tan
Date Deposited:	29 Apr 2026 07:44
Last Modified:	29 Apr 2026 07:44
URI:	https://repositori.buddhidharma.ac.id//id/eprint/3151

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