Tools
Tools
Lee, GM (2017) Data Traffic Model in Machine to Machine Communications over 5G Network Slicing. In: Developments in eSystems Engineering (DeSE) . (DeSE2016, 31 August 2016 - 02 September 2016, Liverpool and Leeds England).
|
Text
Data Traffic Model in Machine to Machine Communications Over 5G Network Slicing-Final Version.pdf - Accepted Version Download (846kB) | Preview |
Abstract
The recent advancements in cellular communication domain have resulted in the emergence of Machine-to-Machine applications, in support of the wide range and coverage provision, low costs, and high mobility. 5G network standards represent a promising technology to support the future of Machine-to-Machine data traffic. In recent years, Human-Type-Communication traffic has seen exponential growth over cellular networks, which resulted in increasing the capacity and higher data rates. These networks are expected to face challenges such as explosion of the data traffic due to the future of smart devices data traffic with various Quality of Service requirements. This paper proposes a novel data traffic aggregation model and algorithm along with a new 5G network slicing based on classification and measuring the data traffic to satisfy Quality of Service for smart systems in a smart city environment. In our proposal, 5G radio resources are efficiently utilized as the smallest unit of a physical resource block in a relay node by aggregating the data traffic of several Machine-to-Machine devices as separate slices based on Quality of Service for each application. OPNET is used to assess the performance of the proposed model. The simulated 5G data traffic classes include file transfer protocol, voice over IP, and video users.
| Item Type: | Conference or Workshop Item (Paper) |
|---|---|
| Uncontrolled Keywords: | Machine-to-Machine; 5G; Network Slicing; Physical Resource Block |
| Subjects: | Q Science > QA Mathematics > QA75 Electronic computers. Computer science |
| Divisions: | Computer Science & Mathematics |
| Publisher: | IEEE |
| Date Deposited: | 31 Aug 2016 08:39 |
| Last Modified: | 13 Apr 2022 15:14 |
| URI: | https://researchonline.ljmu.ac.uk/id/eprint/4063 |
 |
View Item |