Supporting massive M2M traffic in the Internet of Things using millimetre wave 5G network

Al-Falahy, NFA ORCID: https://orcid.org/0000-0001-6133-5048 and Alani, OYK ORCID: https://orcid.org/0000-0002-5848-9107 2017, Supporting massive M2M traffic in the Internet of Things using millimetre wave 5G network , in: 9th Computer Science & Electronic Engineering Conference (CEEC), 27-29 September 2017, University of Essex, UK.

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Abstract

One of the challenges of Fifth Generation (5G) networks is how to support massive number of wireless devices in the Internet of Things (IoT). IoT will interconnect billions of devices under machine-to-machine communication links (M2M). However, this massive connectivity will create burden on the cellular network. Fixed sensors and wearable devices are expected to make the majority of future IoT traffic. And scheduling their huge traffic is the scope of this work. Sensors mobility has been considered with three speed, namely zero speed sensors (fixed), and medium and high speed of 30 and 100kmph, to simulate sensors in vehicles, and model the impact of vehicle mobility on the M2M links. This work defines the dimensionality in terms of the number of M2M devices that can be successfully connected, the required bandwidth, sensors mobility, and the transmission mode used. The mutual use of multi antennas, dense deployment of small cells, and the adoption of millimetre wave band, particularly in the 28GHz have been considered as the key enabling technologies to address the massive traffic generated by IoT. An algorithm has been set to schedule this type of traffic and to show whether the M2M devices completed their traffic upload or failed to reach the margin.

Item Type:	Conference or Workshop Item (Paper)
Schools:	Schools > School of Computing, Science and Engineering
Journal or Publication Title:	9th Computer Science & Electronic Engineering Conference
Publisher:	IEEE
Related URLs:	Publisher
Funders:	Ministry of Higher Education and Scientific Research, Iraq
Depositing User:	NFA Al-Falahy
Date Deposited:	30 Aug 2017 10:05
Last Modified:	15 Feb 2022 22:23
URI:	https://usir.salford.ac.uk/id/eprint/43645

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