Integration and characterisation of the performance of fifth-generation mobile technology (5g) connectivity over the University of Oulu 5g test network (5gtn) for cognitive edge node based on fractal edge platform
Warnakulasuriya, Diluna (2023-05-05)
Warnakulasuriya, Diluna
D. Warnakulasuriya
05.05.2023
© 2023 Diluna Warnakulasuriya. Ellei toisin mainita, uudelleenkäyttö on sallittu Creative Commons Attribution 4.0 International (CC-BY 4.0) -lisenssillä (https://creativecommons.org/licenses/by/4.0/). Uudelleenkäyttö on sallittua edellyttäen, että lähde mainitaan asianmukaisesti ja mahdolliset muutokset merkitään. Sellaisten osien käyttö tai jäljentäminen, jotka eivät ole tekijän tai tekijöiden omaisuutta, saattaa edellyttää lupaa suoraan asianomaisilta oikeudenhaltijoilta.
Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi:oulu-202305051546
https://urn.fi/URN:NBN:fi:oulu-202305051546
Tiivistelmä
In recent years, there has been a growing interest in cognitive edge nodes, which are intelligent devices that can collect and process data at the edge of the network. These nodes are becoming increasingly important for various applications such as smart cities, industrial automation, and healthcare. However, implementing cognitive edge nodes requires a reliable and efficient communication network. Therefore, this thesis assesses the performance of direct cellular (5G) and IEEE 802.11-based Wireless Local Area Network (WLAN) technology for three network architectures, which has the potential to offer low-latency, high-throughput and energy-efficient communication, for cognitive edge nodes.
The study focused on evaluating the network performance metrics of throughput, latency, and power consumption for three different FRACTAL-based network architectures. These architectures include IEEE 802.11-based last mile, direct cellular (5G) backbone, and IEEE 802.11-based last mile over cellular (5G) backbone topologies. This research aims to provide insights into the performance of 5G technology for cognitive edge nodes.
The findings suggest that the power consumption of IEEE 802.11-enabled nodes was only slightly higher than the reference case, indicating that it is more energy-efficient than 5G-enabled nodes. Additionally, in terms of latency, IEEE 802.11 technology may be more favourable. The throughput tests revealed that the cellular (5G) connection exhibited high throughput for communication between a test node and an upper-tier node situated either on the internet or at the network edge. In addition, it was found that the FRACTAL edge platform is flexible and scalable, and it supports different wireless technologies, making it a suitable platform for implementing cognitive edge nodes.
Overall, this study provides insights into the potential of 5G technology and the FRACTAL edge platform for implementing cognitive edge nodes. The results of this research can be valuable for researchers and practitioners working in the field of wireless communication and edge computing, as it sheds light on the feasibility and performance of these technologies for implementing cognitive edge nodes in various applications.
The study focused on evaluating the network performance metrics of throughput, latency, and power consumption for three different FRACTAL-based network architectures. These architectures include IEEE 802.11-based last mile, direct cellular (5G) backbone, and IEEE 802.11-based last mile over cellular (5G) backbone topologies. This research aims to provide insights into the performance of 5G technology for cognitive edge nodes.
The findings suggest that the power consumption of IEEE 802.11-enabled nodes was only slightly higher than the reference case, indicating that it is more energy-efficient than 5G-enabled nodes. Additionally, in terms of latency, IEEE 802.11 technology may be more favourable. The throughput tests revealed that the cellular (5G) connection exhibited high throughput for communication between a test node and an upper-tier node situated either on the internet or at the network edge. In addition, it was found that the FRACTAL edge platform is flexible and scalable, and it supports different wireless technologies, making it a suitable platform for implementing cognitive edge nodes.
Overall, this study provides insights into the potential of 5G technology and the FRACTAL edge platform for implementing cognitive edge nodes. The results of this research can be valuable for researchers and practitioners working in the field of wireless communication and edge computing, as it sheds light on the feasibility and performance of these technologies for implementing cognitive edge nodes in various applications.
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