Name
Abstract | ||
|---|---|---|
Named Data Networking (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. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1109/LATINCOM50620.2020.9282301 | 2020 IEEE Latin-American Conference on Communications (LATINCOM) |
Keywords | DocType | ISSN |
Fowarding Strategy,Deep Reinforcement Learning,DQN,Named Data Networking | Conference | 2330-989X |
ISBN | Citations | PageRank |
978-1-7281-8904-8 | 0 | 0.34 |
References | Authors | |
6 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ygor Amaral B. L. de Sena | 1 | 0 | 0.34 |
| Kelvin Dias | 2 | 26 | 9.61 |
| Cleber Zanchettin | 3 | 0 | 0.34 |