mMTC deployment over sliceable infrastructure : the megasense scenario
|Author:||Motlagh, Naser Hossein1; Afolabi, Ibrahim2; Pozza, Matteo1;|
1Department of Computer Science, University of Helsinki, Helsinki, Finland
2Department of Communications and Networking, Aalto University, Espoo, Finland
3University of Oulu, Oulu, Finland
4Sejong University, Seoul, Korea
5Nokia Bell Labs, Espoo, Finland
|Online Access:||PDF Full Text (PDF, 1.9 MB)|
|Persistent link:|| http://urn.fi/urn:nbn:fi-fe2022022821013
Institute of Electrical and Electronics Engineers,
|Publish Date:|| 2022-02-28
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.
|Pages:||247 - 254|
|Type of Publication:||
A1 Journal article – refereed
|Field of Science:||
113 Computer and information sciences
This work is supported by the Finnish funding agency for research, Business Finland under the 5G Finnish Open Research Collaboration Ecosystem (5GFORCE) project as part of the 5G Test Network Finland (5GTNF). The research was supported in part by Nokia Center for Advanced Research (NCAR) and Healthy Outdoor Premises for Everyone project (UIA03-240). It was also supported in part by Megasense project with grant number 324576, and the Academy of Finland projects: 6Genesis under grant number 318927 and IDEA-MILL with grant numbers 335934 and 335936.
|Academy of Finland Grant Number:||
318927 (Academy of Finland Funding decision)
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