An efficient model for latency optimisation for 5G-enabled IoT applications in smart farming

In various industries, including agriculture, the application of the fifth generation of wireless technology (5G) has led to significant progress. One of the most intriguing aspects of 5G technology is the potential to reduce latency for internet of things (IoT) applications, especially those essential for latency-sensitive smart farming applications. The data generated by IoT devices such as sensors, cameras, and actuators in intelligent farming applications are growing exponentially. Traditional methods of processing and storing IoT data usually include cloud data centres, which are often far from data sources, resulting in multiple network hops that increase latency. To this end, the existing network infrastructures struggle to cope with increasing traffic and to meet the stringent low-latency requirements of various IoT applications. To solve this problem, edge computing has emerged as a solution. Edge technology allows the deployment of 5G core (5GC) network functions close to IoT sensors. This method allows data processing to be performed near the sensor, thereby reducing latency. As a result, this study has proposed an efficient model to minimise latency in 5G networks by moving the user plane function (UPF) node to the edge of the network closer to users by means of the control and user-plane-separation (CUPS) strategy. Furthermore, this study proposed software-defined networking (SDN)-based backhaul. This backhaul was configured to use the open network operating system (ONOS) controller, which has been customised with a distributed core to improve throughput, latency, and scalability. Using SDN in the 5G backhaul network allows operators to create dynamic, scalable, and efficient networks capable of serving a diverse variety of services and applications with varying performance needs. The results of the experiment conducted on the third-generation partnership project (3GPP)-compliant 5G testbed demonstrate that the proposed model reduced the average round-time trip (RTT) by 60.7%, thereby improving the throughput by approximately 40.48%.

Grantor dc:publisher

Tshwane University of Technology

Year dc:date.issued

2024

Author dc:creator

• Makondo, Ntshuxeko

Advisors dc:contributor.supervisor

• Prof Mathonsi, T. E.
• Dr Kobo, H. I.
• Dr du Plessis, D. P.

• Throughput.
• 5G.
• Latency.
• Cloud Computing.
• Edge UPF.
• Cloud UPF.
• Smart Farming.
• SDN.
• CUPS.
• IoT.

Statement dc:rights

• CC0 1.0 Universal

Licence dc:rights.uri

• http://creativecommons.org/publicdomain/zero/1.0/

Language dc:language.iso

en

Handle dc:identifier.uri

https://hdl.handle.net/20.500.14519/2320

OAI identifier oai:identifier

oai:tutvital.tut.ac.za:20.500.14519/2320