2026 International Conference on Networks Computers and Communications (ICNCCom 2026)

Highly dynamic topology, rapid link breakage, and fluctuating density limit the reliability of conventional reactive routing in vehicular ad hoc networks (VANETs). This paper presents a digital twin-driven multi-agent reinforcement learning framework for predictive and multi-objective next-hop selection. The digital twin estimates communication-link lifetime from vehicle position and relative mobility, while each vehicle operates as a Q-learning agent. A weighted reward jointly considers link stability, residual packet lifetime, distance, and congestion. The method was implemented as a custom IPv4 routing module in NS-3 and compared with AODV under IEEE 802.11 communication, 20-40 vehicular nodes, a 300 m radio range, and a 60 s simulation. The proposed framework reduced average end-to-end delay from 0.30 s to 0.10 s and increased packet delivery ratio from 91.4% to 94.8%. The improvement was accompanied by a lower measured throughput of 201.8 kb/s compared with 285.9 kb/s for AODV, indicating a reliability-latency trade-off. The findings support predictive decentralized routing for intelligent transportation networks while identifying throughput and overhead optimization as important extensions.

Vehicular ad hoc networks, digital twin, multi-agent reinforcement learning, multi-objective routing, AODV, NS-3.