Industrial Internet of Things (IIoT) interconnects unconventional objects, such as sensors, actuators, robots, and control systems, with the information systems and the business processes to improve the operational efficiency and productivity. In IIoT, diverse, distributed and huge number of devices are collaborating and connecting over the Internet and the Cloud by generating a high and diverse data rate. In addition, industrial networks will be highly heterogeneous since it will connect heterogeneous devices through various communication technologies. Consequently, the industrial processes set new requirements such as reliability, scalability, and low latency that can not be managed by traditional technologies. The advent of Software Defined Networking (SDN) concept, by decoupling control and data planes, enables flexible and dynamic network architecture management by supporting horizontal scalability through distributed SDN controllers. Moreover, Fog computing is recently emerging as the best technology to provide local processing support with acceptable latency for IIoT devices. In this new rich evolving context, we propose an integration of SDN and Fog computing to provide a flexible and scalable solution granting low delays required by IIoT applications. More precisely, we present a novel architecture for IIoT based on SDN-Fog and then we detail the structure of our proposed Fog node enhanced by SDN. We also show some relevant experimental results that assess the performances of the proposed fog node in terms of latency and throughput.

Bedhief I., Foschini L., Bellavista P., Kassar M., Aguili T. (2019). Toward self-adaptive software defined fog networking architecture for IIoT and industry 4.0. Institute of Electrical and Electronics Engineers Inc. [10.1109/CAMAD.2019.8858499].

Toward self-adaptive software defined fog networking architecture for IIoT and industry 4.0

Foschini L.;Bellavista P.;
2019

Abstract

Industrial Internet of Things (IIoT) interconnects unconventional objects, such as sensors, actuators, robots, and control systems, with the information systems and the business processes to improve the operational efficiency and productivity. In IIoT, diverse, distributed and huge number of devices are collaborating and connecting over the Internet and the Cloud by generating a high and diverse data rate. In addition, industrial networks will be highly heterogeneous since it will connect heterogeneous devices through various communication technologies. Consequently, the industrial processes set new requirements such as reliability, scalability, and low latency that can not be managed by traditional technologies. The advent of Software Defined Networking (SDN) concept, by decoupling control and data planes, enables flexible and dynamic network architecture management by supporting horizontal scalability through distributed SDN controllers. Moreover, Fog computing is recently emerging as the best technology to provide local processing support with acceptable latency for IIoT devices. In this new rich evolving context, we propose an integration of SDN and Fog computing to provide a flexible and scalable solution granting low delays required by IIoT applications. More precisely, we present a novel architecture for IIoT based on SDN-Fog and then we detail the structure of our proposed Fog node enhanced by SDN. We also show some relevant experimental results that assess the performances of the proposed fog node in terms of latency and throughput.
2019
IEEE International Workshop on Computer Aided Modeling and Design of Communication Links and Networks, CAMAD
1
5
Bedhief I., Foschini L., Bellavista P., Kassar M., Aguili T. (2019). Toward self-adaptive software defined fog networking architecture for IIoT and industry 4.0. Institute of Electrical and Electronics Engineers Inc. [10.1109/CAMAD.2019.8858499].
Bedhief I.; Foschini L.; Bellavista P.; Kassar M.; Aguili T.
File in questo prodotto:
Eventuali allegati, non sono esposti

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/11585/742303
 Attenzione

Attenzione! I dati visualizzati non sono stati sottoposti a validazione da parte dell'ateneo

Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 37
  • ???jsp.display-item.citation.isi??? 6
social impact