A QoS Optimization Technique with Deep Reinforcement Learning in SDN-Based IoT

  • Mohammadreza Moslehi Department of Computer Engineering, University of Kashan, Kashan, Iran.
  • Hossein Ebrahimpor-Komleh Department of Computer Engineering, University of Kashan, Kashan, Iran.
  • Salman Goli Department of Computer Engineering, University of Kashan, Kashan, Iran.
  • Reza Taji Independent Researcher in the Field of AI and Neural Networking.
Keywords: Internet of Things, Software-Defined Networking (SDN), Deep Reinforcement Learning, QoS


In recent years, exponential growth of communication devices in Internet of Things (IoT) has become an emerging technology which facilitates heterogeneous devices to connect with each other in heterogeneous networks. This communication requires different level of Quality-of-Service (QoS) and policies depending on the device type and location. To provide a specific level of QoS, we can utilize emerging new technological concepts in IoT infrastructure, software-defined network (SDN) and, machine learning algorithms. We use deep reinforcement learning in the process of resource management and allocation in control plane. We present an algorithm that aims to optimize resource allocation. Simulation results show that the proposed algorithm improved network performances in terms of QoS parameters, including delay and throughput compared to Random and Round Robin methods. Compared to similar methods the performance of the proposed method is also as good as the fuzzy and predictive methods.


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How to Cite
Moslehi, M., Ebrahimpor-Komleh, H., Goli, S., & Taji, R. (2021). A QoS Optimization Technique with Deep Reinforcement Learning in SDN-Based IoT. Majlesi Journal of Electrical Engineering, 15(3), 105-113. https://doi.org/https://doi.org/10.52547/mjee.15.3.105