So far, research on Smart Cities and self-organizing networking techniques for 5G cellular systems has been one-sided: a Smart City relies on 5G to support massive M2M communications, but the actual network is unaware of the information flowing through it. However, a greater synergy between the two would make the relationship mutual, since the insights provided by the massive amount of data gathered by sensors can be exploited to improve the communication performance. In this work, we concentrate on self-organization techniques to improve handover efficiency using vehicular traffic data gathered in London. Our algorithms exploit mobility patterns between cell coverage areas and road traffic congestion levels to optimize the handover bias in HetNets and dynamically manage mme loads to reduce handover completion time

Using Smart City Data in 5G Self-Organizing Networks

DALLA CIA, MASSIMO;MASON, FEDERICO;PERON, DAVIDE;Chiariotti, Federico;Polese, Michele;Zorzi, Michele;Zanella, Andrea
2017

Abstract

So far, research on Smart Cities and self-organizing networking techniques for 5G cellular systems has been one-sided: a Smart City relies on 5G to support massive M2M communications, but the actual network is unaware of the information flowing through it. However, a greater synergy between the two would make the relationship mutual, since the insights provided by the massive amount of data gathered by sensors can be exploited to improve the communication performance. In this work, we concentrate on self-organization techniques to improve handover efficiency using vehicular traffic data gathered in London. Our algorithms exploit mobility patterns between cell coverage areas and road traffic congestion levels to optimize the handover bias in HetNets and dynamically manage mme loads to reduce handover completion time
File in questo prodotto:
File Dimensione Formato  
SON_IoT_2017.pdf

accesso aperto

Tipologia: Postprint (accepted version)
Licenza: Accesso libero
Dimensione 5.03 MB
Formato Adobe PDF
5.03 MB Adobe PDF Visualizza/Apri
Pubblicazioni consigliate

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11577/3248094
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 42
  • ???jsp.display-item.citation.isi??? 31
social impact