Q-Learning Based Forwarding Strategy in Named Data Networks

Abstract

Named Data Networking (NDN) emerged as a promising new communication architecture aimed to cope with the need for efficient and robust data dissemination. NDN forwarding strategy plays a significant role for efficient data dissemination. Most of the currently deployed forwarding strategies use fixed control rules given by the routing layer. Obviously these simplified rules are inaccurate in dynamically changing networks. In this paper, we propose a novel Interest forwarding scheme called Q-Learning based Forwarding Strategy (QLFS). QLFS embedded a continual and online learning process that ensures quick reaction to sudden disruption during network operation. At each NDN router, forwarding decisions are continually adapted according to delivery times variation and perceived events, i. e. NACK reception, Interest Timeout... Our simulation results show that our proposed approach is more efficient than state of the art forwarding strategy in term of data delivery and number of timeout events.