Measurement and Classification of Smart Systems Data Traffic Over 5G Mobile Networks

Mohammed Dighriri, Gyu Myoung Lee, Thar Baker

Research output: Chapter in Book/Conference proceeding with ISSN or ISBNChapterpeer-review

Abstract

Machine-to-Machine communication is rapidly growing and becoming a significant part of the current 4G and future 5G mobile network data traffic, which is intended to provide coverage support and lower costs for mobile network providers. The 5G mobile network represents a promising technology to support the future of Machine-to-Machine communications. In recent years, smart devices, such as smartphones and traffic monitoring systems, have experienced exponential growth over mobile networks with different radio resource usage. This has caused massive challenges as a result of simultaneous access data traffic as well as a large number of devices sending small-sized data. This chapter proposes a novel data traffic aggregation and slicing model with algorithms in 5G uplink, based on classifying and measuring the data traffic to achieve quality of service for smart systems. Moreover, 5G radio resources are efficiently shared by several smart devices in a relay node by aggregating incoming data traffic based on quality of service.
Original languageEnglish
Title of host publicationTechnology for Smart Futures
Pages195–217
Number of pages22
ISBN (Electronic)9783319601373
DOIs
Publication statusPublished - 6 Sep 2017

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