mMTC Deployment over Sliceable Infrastructure: The Megasense Scenario

Naser Hossein Motlagh, Ibrahim Afolabi, Matteo Pozza, Miloud Bagaa, Tarik Taleb, Sasu Tarkoma, Hannu Flinck

Research output: Contribution to journalArticleScientificpeer-review

6 Citations (Scopus)
65 Downloads (Pure)


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.

Original languageEnglish
Pages (from-to)247-254
Number of pages8
Issue number6
Early online date2021
Publication statusPublished - 2021
MoE publication typeA1 Journal article-refereed


  • 5G mobile communication
  • Air pollution
  • Cloud computing
  • Intelligent sensors
  • Network slicing
  • Sensors
  • Wireless sensor networks


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  • IDEA-MILL: Industrial Distributed Edge Architecture over Machine Intelligence for Local Learning

    Taleb, T.


    Project: Academy of Finland: Other research funding

  • 5G-FORCE-Taleb

    Taleb, T., Addad, R., Amor, A., Afolabi, I., Khennouche, H., Kerfah, I. & Batouche, A.


    Project: Business Finland: Other research funding

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