Stammdaten

Artificial-Intelligence-Based Performance Enhancement of the G3-PLC LOADng Routing Protocol for Sensor Networks
Untertitel:
Kurzfassung:

Powerline Communications (PLC) is a popular technology providing infrastructure for applications related to IoT, smart grids, smart cities, in-home networking and has been experimentally considered for broadband access. Sensor networks and Automatic Meter Reading applications are closely related to this technology, as it provides free infrastructure and sustains the data rate requirements. The application here considered consists in the implementation of the G3-PLC LOADng routing protocol in the nodes of a sensor/meter network, where the nodes share all the same medium. G3-PLC is a powerline communication standard, employing OFDM at the physical layer and oriented at enabling the smart grid vision. The Medium Access Control implements CSMA/CA, while the Logical Link Control implements LOADng routing, which is the ITU-T G.9903 recommended specification for Lossy and Low-power Networks (LLNs). In this paper, we consider the mapping phase of the routing protocol, in which the central element of the network establishes the routes to reach any node. By simulating this process via a physical simulation tool, it is possible to synthetically train an Artificial Neural Network and teach it how the optimally established routes correlate to the topological and geometrical properties of the network. Eventually, we discuss how, by employing this AI approach, it is possible to speed-up the routing mapping process.

Schlagworte:
Publikationstyp: Beitrag in Proceedings (Autorenschaft)
Art der Veröffentlichung Online Publikation
Erschienen in: IEEE International Symposium on Power Line Communications and its Applications 2019 (ISPLC 2019)
IEEE International Symposium on Power Line Communications and its Applications 2019 (ISPLC 2019)
zur Publikation
 ( IEEE ComSoc; )
Erscheinungdatum: 03.04.2019
Titel der Serie: -
Bandnummer: -
Erstveröffentlichung: Ja
Version: -
Seite: S. 1 - 6

Identifikatoren

ISBN: -
ISSN: -
DOI: http://dx.doi.org/10.1109/ISPLC.2019.8693390
AC-Nummer: -
Homepage: https://ieeexplore.ieee.org/document/8693390
Open Access
  • Online verfügbar (nicht Open Access)

Zuordnung

Organisation Adresse
Fakultät für Technische Wissenschaften
 
Institut für Vernetzte und Eingebettete Systeme
Universitätsstraße 65-67
9020  Klagenfurt am Wörthersee
Österreich
  -993640
   kornelia.lienbacher@aau.at
https://nes.aau.at/
zur Organisation
Universitätsstraße 65-67
AT - 9020  Klagenfurt am Wörthersee

Kategorisierung

Sachgebiete
Forschungscluster
  • Energiemanagement und -technik
Peer Reviewed
  • Ja
Publikationsfokus
  • Science to Science (Qualitätsindikator: I)
Klassifikationsraster der zugeordneten Organisationseinheiten:
Arbeitsgruppen
  • Embedded Communication Systems Group

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