Paper Info
Title
Deep-Reinforcement-Learning-Based QoS-Aware Secure Routing for SDN-IoT
Abstract
Recently, with the proliferation of communication devices, Internet of Things (IoT) has become an emerging technology which facilitates massive devices to be enabled with connectivity by heterogeneous networks. However, it is usually a technical challenge for traditional networks to handle such a huge number of devices in an efficient manner. Recently, the software-defined network (SDN) technique with its agility and elasticity has been incorporated into IoT to meet the potential scale and flexibility requirements and form a novel IoT architecture also known as SDN-IoT. As the size of SDN-IoT increases, efficient routing protocols with low latency and high security are required, while the default routing protocols of SDN are still vulnerable to dynamic change of flow control rules especially when the network is under attack. To address the above issues, a deep-reinforcement-learning-based quality-of-service (QoS)-aware secure routing protocol (DQSP) is proposed in this article. While guaranteeing the QoS, our method can extract knowledge from history traffic demands by interacting with the underlying network environment, and dynamically optimize the routing policy. Extensive simulation experiments have been conducted with respect to several network performance metrics, demonstrating that our DQSP has good convergence and high effectiveness. Moreover, DQSP outperforms the traditional OSPF routing protocol, at least 10% relative performance gains in most cases.
Year
DOI
Venue
2020
10.1109/JIOT.2019.2960033
IEEE Internet of Things Journal
Keywords
DocType
Volume
Routing,Quality of service,Routing protocols,Security,Internet of Things,Reinforcement learning,Control systems
Journal
7
Issue
ISSN
Citations 
7
2327-4662
3
PageRank 
References 
Authors
0.39
0
4
Name
Order
Citations
PageRank
Xuancheng Guo130.39
Hui Lin2216.08
Zhi-yang Li314626.37
Min Peng461.77