A Review on Forwarding Strategies in NDN based Vehicular Networks

Named Data Networking (NDN) is a model that has been proposed by many researchers to alter the long-established IP based networking model. It derives the content centric approach rather than host-based approach. This is gaining even more traction in the wireless network and is able to replace the conventional IP-based networking. Up to now, NDN has proven to be fruitful when used with certain limitations in vehicular networks. Vehicular networks deal with exchanging information across fast moving complex vehicle network topology. The sending and receiving of information in such a scenario acts as a challenge and thus requires an effective forwarding strategy to address this problem. Different research work has provided with multiple forwarding strategy that solves the current problem up to some limit but further research work is still longed for to get an optimum solution. This paper provides a brief survey on current existing forwarding strategies related to vehicular networks using NDN as well as providing information on various resources and technologies used in it.

Stochastic Adaptive Forwarding Strategy Based on Deep Reinforcement Learning for Secure Mobile Video Communications in NDN

Named Data Networking (NDN) can effectively deal with the rapid development of mobile video services. For NDN, selecting a suitable forwarding interface according to the current network status can improve the efficiency of mobile video communication and can also avoid attacks to improve communication security. For this reason, we propose a stochastic adaptive forwarding strategy based on deep reinforcement learning (SAF-DRL) for secure mobile video communications in NDN. For each available forwarding interface, we introduce the twin delayed deep deterministic policy gradient algorithm to obtain a more robust forwarding strategy. Moreover, we conduct various numerical experiments to validate the performance of SAF-DRL. Compared with BR, RFA, SAF, and AFSndn forwarding strategies, the results show that SAF-DRL can reduce the delivery time and the average number of lost packets to improve the performance of NDN.

Intelligent Forwarding Strategy for Congestion Control Using Q-Learning and LSTM in Named Data Networking

Named data networking (NDN) is a future network architecture that replaces IP-oriented communication with content-oriented communication and has new features such as cache, multiple paths, and multiple sources. Services such as video streaming, to which NDN can be applied in the future, can cause congestion if data is concentrated on one of the nodes during high demand. To solve this problem, sending rate control methods such as TCP congestion control have been proposed, but they do not adequately reflect the characteristics of NDN. Therefore, we use reinforcement learning and deep learning to propose a congestion control method that takes advantage of multipath features. The intelligent forwarding strategy for congestion control using Q-learning and long short-term memory in NDN proposed in this paper is divided into two phases. The first phase uses an LSTM model to train a pending interest table (PIT) entry rate that can be used as an indicator to detect congestion by knowing the amount of data returned. In the second phase, it is forwarded to an alternative path that is not congestive via Q-learning based on the PIT entry rate predicted by the trained LSTM model. The simulation results show that the proposed method increases the data reception rate by 6.5% and 19.5% and decreases the packet drop rate by 7.3% and 17.2% compared to an adaptive SRTT-based forwarding strategy (ASF) and BestRoute.