Open Access iconOpen Access



Intelligent Real-Time IoT Traffic Steering in 5G Edge Networks

Sa Math1, Prohim Tam1, Seokhoon Kim2,*

1 Department of Software Convergence, Soonchunhyang University, Asan, 31538, Korea
2 Department of Computer Software Engineering, Soonchunhyang University, Asan, 31538, Korea

* Corresponding Author: Seokhoon Kim. Email: email

Computers, Materials & Continua 2021, 67(3), 3433-3450.


In the Next Generation Radio Networks (NGRN), there will be extreme massive connectivity with the Heterogeneous Internet of Things (HetIoT) devices. The millimeter-Wave (mmWave) communications will become a potential core technology to increase the capacity of Radio Networks (RN) and enable Multiple-Input and Multiple-Output (MIMO) of Radio Remote Head (RRH) technology. However, the challenging key issues in unfair radio resource handling remain unsolved when massive requests are occurring concurrently. The imbalance of resource utilization is one of the main issues occurs when there is overloaded connectivity to the closest RRH receiving exceeding requests. To handle this issue effectively, Machine Learning (ML) algorithm plays an important role to tackle the requests of massive IoT devices to RRH with its obvious capacity conditions. This paper proposed a dynamic RRH gateways steering based on a lightweight supervised learning algorithm, namely K-Nearest Neighbor (KNN), to improve the communication Quality of Service (QoS) in real-time IoT networks. KNN supervises the model to classify and recommend the user’s requests to optimal RRHs which preserves higher power. The experimental dataset was generated by using computer software and the simulation results illustrated a remarkable outperformance of the proposed scheme over the conventional methods in terms of multiple significant QoS parameters, including communication reliability, latency, and throughput.


Cite This Article

APA Style
Math, S., Tam, P., Kim, S. (2021). Intelligent real-time iot traffic steering in 5G edge networks. Computers, Materials & Continua, 67(3), 3433-3450.
Vancouver Style
Math S, Tam P, Kim S. Intelligent real-time iot traffic steering in 5G edge networks. Comput Mater Contin. 2021;67(3):3433-3450
IEEE Style
S. Math, P. Tam, and S. Kim "Intelligent Real-Time IoT Traffic Steering in 5G Edge Networks," Comput. Mater. Contin., vol. 67, no. 3, pp. 3433-3450. 2021.

cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
  • 2385


  • 1395


  • 0


Share Link