Title

BIRD: Bio-Inspired Distributed Interest Forwarding in Vehicular Named-Data Networks

Document Type

Contribution to Book

Publication Date

4-9-2018

Publication Title

Proceedings of the Association for Computing Machinery Symposium on Applied Computing

DOI

10.1145/3167132.3167355

ISBN

978-1-4503-5191-1

Abstract

In this work we tackle the problem of congestion and Interest broadcast storm problem in vehicular named data networks (VNDN) and propose a bio-inspired approach which makes the Interest forwarding self-adaptive and autonomous. The properties like scalability, self-adaptiveness, and simplicity are inherently available to the biological species. These properties are desirable in the VNDN environment, who face the daunting issue of Interest flooding and congestion. The proposed Bio-Inspired Distributed (BIRD) Interest forwarding scheme allows the on-road vehicles to make intelligent Interest forwarding decisions based on the simple rules followed birds in nature. The Interest packets are guided through multiple paths in a flock like manner towards the provider. Simulation results show that at the cost of additional packets for multiple paths we achieve an average of 20% higher Content satisfaction ratio from both RUFS and NAIF. Additionally, BIRD incurs 10% less delay as compared to the multi-path NIAF scheme in urban scenarios with varying network density.

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