Abstract—Vehicular Named Data Networking (V-NDN) is a promising solution for improving data communication in high-mobility vehicular environments. This study compares three forwarding strategies—Best-route, Multicast-VANET, and Adaptive Strategy Forwarding (ASF)—and implements Best-route and ASF in a V-NDN network. Performance is evaluated based on Satisfied Interests, CS Hit Ratio, Unsolicited Data, and Incoming Data Matching. Simulations show that ASF outperforms the other two methods. It achieves the maximum Interest fulfilment ratio with no Unsatisfied Interests, the highest CS Hit Ratio (0.74), and the lowest CS Miss Ratio (0.28), indicating optimal caching efficiency. ASF also handles up to 10,146 Interests per maximum period, far surpassing Best-route (2,978) and Multicast-VANET (2,946). ASF further demonstrates superiority in Incoming Data Matching, achieving the highest total (2,749) among the three methods. Based on these findings, ASF is concluded to be the most effective forwarding strategy for V-NDN networks, particularly in high-density and complex network scenarios. These findings have significant implications for the design and deployment of efficient and reliable vehicular communication systems, paving the way for advanced applications such as autonomous driving and connected vehicles.
 

Keywords—Vehicular Named Data Networking (V-NDN), forwarding strategies, adaptive forwarding strategy, forwarding pipeline, interest and data measurement


Cite: Syaiful Ahdan, Iskandar, and Nana Rachmana Syambas, “Towards Efficient Data Communication in Vehicular NDN: The Superiority of Adaptive SRTT-Based Forwarding (ASF)," Journal of Communications, vol. 20, no. 4, pp. 415-435, 2025.

Copyright © 2025 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).