Optimizing Self-Learning Forwarding Strategies in Vehicular Named Data Network through Protocol Simplification

Optimizing Self-Learning Forwarding Strategies in Vehicular Named Data Network through Protocol Simplification

연구 분야: Networking

학회: 2024 4th International Conference of Science and Information Technology in Smart Administration (ICSINTESA)

The Information Centric Network (ICN) concept gave rise to the Named Data Network (NDN), a content-centric computer network architecture. Vehicular ad-hoc Networks (VANETs), characterized by high mobility, have embraced NDN, employing broadcast-based forwarding strategies. This self-learning approach enables adaptive path adjustments without relying on explicit routing instructions, a valuable trait in dynamic wireless environments. However, node movements are interpreted as link failures, prompting nodes to issue Negative Acknowledgments (NACKs) to consumers. Yet, due to changes in node positions, NACKs might lose their way back to the intended consumers, leading to increased transmission overhead. To address these issues, this paper proposes a streamlined communication protocol for self-learning forwarding strategies. The suggested approach mitigates the overhead associated with negative acknowledgments, thereby reducing the delay in re-transmitting failed packets. Simulations conducted in ndnSIM 2.8 compare the performance of this scheme against existing strategies (such as multicast, multicast VANET, and self-learning) across scenarios with varying numbers of nodes. Results indicate that the proposed scheme achieves a lower round-trip time (approximately \mathbf{1 2. 8 9 \%} ) and a higher throughput (around \mathbf{1 6. 0 2 \%} ) compared to the default self-learning approach as the number of nodes increases. This improvement stems from the reduction in negative acknowledgment protocol overhead within the self-learning forwarding strategy, enabling more efficient retrieval of successful packets.