NDN has gained significant attention due to the appearance of several unforeseen design flaws that became evident with new communication scenarios. Among its many features, the two standard NDN forwarding strategies are not adaptive, causing performance degradation in several scenarios. This paper proposes an adaptive forwarding strategy based on deep reinforcement learning with Deep Q-Network, which analyzes the NDN router interface metrics without creating signaling overhead or harming the design principles from the NDN architecture, besides showing significant performance gains compared to the standard strategies.
@article{arxiv.2010.09897,
title = {DQN-AF: Deep Q-Network based Adaptive Forwarding Strategy for Named Data Networking},
author = {Ygor Amaral B. L. de Sena and Kelvin Lopes Dias and Cleber Zanchettin},
journal= {arXiv preprint arXiv:2010.09897},
year = {2020}
}
Comments
Accepted to be published in Proceedings of the 2020 IEEE Latin-American Conference on Communications (IEEE LATINCOM 2020), Nov 18-20, 2020