Energy Profiling and Analysis of 5G Private Networks: Evaluating Energy Consumption Patterns
Islam, Johirul; Ahmad, Ijaz; Gimhana, Shakthi; Markkula, Juho; Harjula, Erkki (2025-03-31)
ACM
31.03.2025
Islam, J., Ahmad, I., Gimhana, S., Markkula, J., & Harjula, E. (2024). Energy profiling and analysis of 5g private networks: Evaluating energy consumption patterns. Proceedings of the 14th International Conference on the Internet of Things, 246–247. https://doi.org/10.1145/3703790.3703832
https://creativecommons.org/licenses/by/4.0/
© 2024 Copyright held by the owner/author(s). This work is licensed under a Creative Commons Attribution International 4.0 License.
https://creativecommons.org/licenses/by/4.0/
© 2024 Copyright held by the owner/author(s). This work is licensed under a Creative Commons Attribution International 4.0 License.
https://creativecommons.org/licenses/by/4.0/
Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi:oulu-202504092489
https://urn.fi/URN:NBN:fi:oulu-202504092489
Tiivistelmä
Abstract
Private 5G networks provide enhanced security, a wide range of optimized services through network slicing, reduced latency, and support for many IoT devices in a specific area, all under the owner’s full control. Higher security and privacy to protect sensitive data is the most significant advantage of private networks, in e.g., smart hospitals. For long-term sustainability and cost-effectiveness of private 5G networks, analyzing and understanding the energy consumption variation holds a greater significance in reaching toward green private network architecture for 6G. This paper addresses this research gap by providing energy profiling of network components using an experimental laboratory setup that mimics real private 5G networks under various network conditions, which is a missing aspect in the existing literature.
Private 5G networks provide enhanced security, a wide range of optimized services through network slicing, reduced latency, and support for many IoT devices in a specific area, all under the owner’s full control. Higher security and privacy to protect sensitive data is the most significant advantage of private networks, in e.g., smart hospitals. For long-term sustainability and cost-effectiveness of private 5G networks, analyzing and understanding the energy consumption variation holds a greater significance in reaching toward green private network architecture for 6G. This paper addresses this research gap by providing energy profiling of network components using an experimental laboratory setup that mimics real private 5G networks under various network conditions, which is a missing aspect in the existing literature.
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