5G Network Segmantation for IoT Applications: Perspective on Scalability and Flexibility Growth by 2025 Times

As the applications of Internet of Things (IoT) are in exponential rise, there has been increasing emphasis on large, elastic and adaptable networking. It is difficult to fulfill the various demands of IoT devices at the same time due to the limitations of traditional networks regarding latency, bandwidth, and energy consumption. Network slicing in the 5G context provides an opportunity to launch separate virtual isolated networks for different purposes. This research aims at evaluating the applicability of 5G network slicing for handling scalability and flexibility issues in IoT applications by 2025 with special attention to network performance. To investigate the 5G network slicing performance of the IoT use cases, we deployed a simulation approach considering smart city, industrial automation, and healthcare application. Specific performance parameters, which include latency, bandwidth consumption and scale were recorded against traditional networks implementations. Compared to the standard networks, the technical advantages of 5G network slicing were seen in a 40% higher latency and a 30% better Bandwidth on Demand. New approaches to scalability enhanced by 50% facilitating incorporation of devices totaling to 10,000 dev/km2 including low power IoT devices energy requirement into account. Scalability and flexibility, network slicing in 5G network, IoT applications are expected to have growth of around 2025 times more and customized most suited mobile networks will be made to improve overall IoT performance.

Authors

Publication Details

Journal
Radioelectronics Nanosystems Information Technologies
Published
2025-04-15
DOI
https://doi.org/10.17725/j.rensit.2025.17.229
Primary Topic
IoT and Edge/Fog Computing
Type
article
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article

5G Network Segmantation for IoT Applications: Perspective on Scalability and Flexibility Growth by 2025 Times

Nameer Hashim Qasim
Radioelectronics Nanosystems Information Technologies
IoT and Edge/Fog Computing
article

5G Network Segmantation for IoT Applications: Perspective on Scalability and Flexibility Growth by 2025 Times

Nameer Hashim Qasim
article en

Abstract

As the applications of Internet of Things (IoT) are in exponential rise, there has been increasing emphasis on large, elastic and adaptable networking. It is difficult to fulfill the various demands of IoT devices at the same time due to the limitations of traditional networks regarding latency, bandwidth, and energy consumption. Network slicing in the 5G context provides an opportunity to launch separate virtual isolated networks for different purposes. This research aims at evaluating the applicability of 5G network slicing for handling scalability and flexibility issues in IoT applications by 2025 with special attention to network performance. To investigate the 5G network slicing performance of the IoT use cases, we deployed a simulation approach considering smart city, industrial automation, and healthcare application. Specific performance parameters, which include latency, bandwidth consumption and scale were recorded against traditional networks implementations. Compared to the standard networks, the technical advantages of 5G network slicing were seen in a 40% higher latency and a 30% better Bandwidth on Demand. New approaches to scalability enhanced by 50% facilitating incorporation of devices totaling to 10,000 dev/km2 including low power IoT devices energy requirement into account. Scalability and flexibility, network slicing in 5G network, IoT applications are expected to have growth of around 2025 times more and customized most suited mobile networks will be made to improve overall IoT performance.

Radioelectronics Nanosystems Information TechnologiesVol. 17(2)
Openalex Percentile: Top 93%
IoT and Edge/Fog Computing
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5G Network Segmantation for IoT Applications: Perspective on Scalability and Flexibility Growth by 2025 Times — Nameer Hashim Qasim · Radioelectronics Nanosystems Information Technologies (2025) | TGRS Research Map | TGRS