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Measuring End-to-End Delay in Low Energy SDN IoT Platform

Mykola Beshley1, Natalia Kryvinska2,*, Halyna Beshley1, Orest Kochan1, Leonard Barolli3

1 Department of Telecommunications, Lviv Polytechnic National University, 79013 Lviv, Ukraine
2 Department of Information Systems, Faculty of Management, Comenius University in Bratislava, 82005 Bratislava, Slovakia
3 Department of Information and Communication Engineering, Faculty of Information Engineering, Fukuoka Institute of Technology (FIT), 3-30-1 Wajiro-Higashi, Higashi-Ku, Fukuoka 811-0295, Japan

* Corresponding Author: Natalia Kryvinska. Email:

(This article belongs to this Special Issue: Green IoT Networks using Machine Learning, Deep Learning Models)

Computers, Materials & Continua 2022, 70(1), 19-41.


In this paper, we developed a new customizable low energy Software Defined Networking (SDN) based Internet of Things (IoT) platform that can be reconfigured according to the requirements of the target IoT applications. Technically, the platform consists of a set of low cost and energy efficient single-board computers, which are interconnected within a network with the software defined configuration. The proposed SDN switch is deployed on Raspberry Pi 3 board using Open vSwitch (OvS) software, while the Floodlight controller is deployed on the Orange Pi Prime board. We firstly presented and implemented the method for measuring a delay introduced by each component of the IoT infrastructure, ranging from the sensor, the core of SDN, the IoT broker, to an IoT subscriber. Thus, we presented the approach for estimating energy efficiency for SDN based IoT platform proportional to the traffic. The experiments carried out on a real SDN topology based on single-board computers show that our approach not only saves up to 53.56% of energy at low traffic intensity, but also provides QoS guarantee for IoT applications.


Cite This Article

M. Beshley, N. Kryvinska, H. Beshley, O. Kochan and L. Barolli, "Measuring end-to-end delay in low energy sdn iot platform," Computers, Materials & Continua, vol. 70, no.1, pp. 19–41, 2022.

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.
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