mMTC Deployment over Sliceable Infrastructure: The Megasense Scenario

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openAccess
Journal Title
Journal ISSN
Volume Title
A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
Date
2021
Major/Subject
Mcode
Degree programme
Language
en
Pages
Series
IEEE NETWORK
Abstract
Massive Machine Type Communication (mMTC) has long been identified as a major vertical sector and enabler of the industry 4.0 technological evolution that will seamlessly ease the dynamics of machine-to-machine communications while leveraging 5G technology. To advance this concept, we have developed and tested an mMTC network slice called Megasense. Megasense is a complete framework that consists of multiple software modules, which is used for collecting and analyzing air pollution data that emanates from a massive amount of air pollution sensors. Taking advantage of 5G networks, Megasense will significantly benefit from an underlying communication network that is traditionally elastic and can accommodate the on-demand changes in requirements of such a use case. As a result, deploying the sensor nodes over a sliceable 5G system is deemed the most appropriate in satisfying the resource requirements of such a use case scenario. In this light, in order to verify how 5G-ready our Megasense solution is, we deployed it over a network slice that is totally composed of virtual resources. We have also evaluated the impact of the network slicing platform on Megasense in terms of bandwidth and resource utilization. We further tested the performances of the Megasense system and come up with different deployment recommendations based on which the Megasense system would function optimally.
Description
Publisher Copyright: IEEE
Keywords
5G mobile communication, Air pollution, Cloud computing, Intelligent sensors, Network slicing, Sensors, Wireless sensor networks
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Citation
Motlagh, N H, Afolabi, I, Pozza, M, Bagaa, M, Taleb, T, Tarkoma, S & Flinck, H 2021, ' mMTC Deployment over Sliceable Infrastructure : The Megasense Scenario ', IEEE Network, vol. 35, no. 6, pp. 247-254 . https://doi.org/10.1109/MNET.111.2100093