Big Data in 5G distributed applications

Nejkovic, Valentina (Faculty of Electronic Engineering, University of Nis, Nis, Serbia) ; Visa, Ari (Faculty of Computing and Electrical Engineering, Tampere University of Technology, Tampere, Finland) ; Tosic, Milorad (Faculty of Electronic Engineering, University of Nis, Nis, Serbia) ; Petrovic, Nenad (Faculty of Electronic Engineering, University of Nis, Nis, Serbia) ; Valkama, Mikko (Faculty of Computing and Electrical Engineering, Tampere University of Technology, Tampere, Finland) ; Koivisto, Mike (Faculty of Computing and Electrical Engineering, Tampere University of Technology, Tampere, Finland) ; Talvitie, Jukka (Faculty of Computing and Electrical Engineering, Tampere University of Technology, Tampere, Finland) ; Rancic, Svetozar (Faculty of Science and Mathematics, University of Nis, Nis, Serbia) ; Grzonka, Daniel (Cracow University of Technology, Cracow, Poland) ; Tchorzewski, Jacek (Cracow University of Technology, Cracow, Poland) ; Kuonen, Pierre (School of Engineering and Architecture (HEIA-FR), HES-SO // University of Applied Sciences Western Switzerland) ; Gortazar, Francisco (Rey Juan Carlos University, Madrid, Spain)

Fifth generation mobile networks (5G) will rather supplement than replace current 4G networks by dramatically improving their bandwidth, capacity and reliability. This way, much more demanding use cases that simply are not achievable with today’s networks will become reality - from home entertainment, to product manufacturing and healthcare. However, many of them rely on Internet of Things (IoT) devices equipped with low-cost transmitters and sensors that generate enormous amount of data about their environment. Therefore, due to large scale of 5G systems, combined with their inherent complexity and heterogeneity, Big Data and analysis techniques are considered as one of the main enablers of future mobile networks. In this work, we recognize 5G use cases from various application domains and list the basic requirements for their development and realization.


Keywords:
Faculty:
Ingénierie et Architecture
School:
HEIA-FR
Institute:
iCoSys - Institut des systèmes complexes
Publisher:
Cham, Springer
Date:
2019-03
Cham
Springer
Pagination:
25 p.
Published in:
High-performance modelling and simulation for big data applications
Series Statement:
Lecture Notes in Computer Science
DOI:
ISSN:
0302-9743
ISBN:
978-3-030-16271-9
External resources:
Appears in Collection:



 Record created 2019-12-17, last modified 2020-01-07

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