Driven by the fourth industrial revolution (Industry 4.0), future and emerging Internet of Things (IoT) technologies will be required to support unprecedented services and demanding applications for massive machine-type connectivity, with low latency, high reliability and distributed information processing capability. In this article, distributed signal processing methodologies are highlighted as enablers for next generation cloud-assisted IoT systems. The proposed distributed algorithms run inside a wireless cloud network (WCN) platform and are exploited for WCN self-organization, distributed synchronization, networking and sensing. The WCN can lease augmented communication and sensing services to off-the-shelf industrial wireless devices via a dense, self-organizing “cloud” of wireless nodes. The paper introduces, at first, the WCN architecture and illustrates an experimental case study inside a pilot industrial plant. Next, it proposes a re-design of consensus-based algorithms for enabling a selected set of distributed information processing functionalities within the WCN platform, with application in practical IoT scenarios. In particular, cooperative communication algorithms are adopted to support reliable communication services. Distributed timing and carrier frequency offset estimation methods are investigated to enable low-latency services through accurate synchronization. Autonomous identification of recurring interference patterns is proposed for multiple access coordination in the shared 5G spectrum. Finally, localization and vision applications based on distributed processing of wireless signals are investigated to support contact-free human–machine interfaces.

Distributed signal processing for dense 5G IoT platforms: Networking, synchronization, interference detection and radio sensing

Soatti, Gloria;Savazzi, Stefano;Nicoli, Monica;Alvarez, Maria Antonieta;Kianoush, Sanaz;Rampa, Vittorio;Spagnolini, Umberto
2019

Abstract

Driven by the fourth industrial revolution (Industry 4.0), future and emerging Internet of Things (IoT) technologies will be required to support unprecedented services and demanding applications for massive machine-type connectivity, with low latency, high reliability and distributed information processing capability. In this article, distributed signal processing methodologies are highlighted as enablers for next generation cloud-assisted IoT systems. The proposed distributed algorithms run inside a wireless cloud network (WCN) platform and are exploited for WCN self-organization, distributed synchronization, networking and sensing. The WCN can lease augmented communication and sensing services to off-the-shelf industrial wireless devices via a dense, self-organizing “cloud” of wireless nodes. The paper introduces, at first, the WCN architecture and illustrates an experimental case study inside a pilot industrial plant. Next, it proposes a re-design of consensus-based algorithms for enabling a selected set of distributed information processing functionalities within the WCN platform, with application in practical IoT scenarios. In particular, cooperative communication algorithms are adopted to support reliable communication services. Distributed timing and carrier frequency offset estimation methods are investigated to enable low-latency services through accurate synchronization. Autonomous identification of recurring interference patterns is proposed for multiple access coordination in the shared 5G spectrum. Finally, localization and vision applications based on distributed processing of wireless signals are investigated to support contact-free human–machine interfaces.
Dense cooperative networks
Consensus algorithms
Distributed signal processing
Distributed synchronization
Internet of things
File in questo prodotto:
File Dimensione Formato  
RV_2019_ad_hoc_post_print.pdf

accesso aperto

Descrizione: Full paper post-print
: Post-Print (DRAFT o Author’s Accepted Manuscript-AAM)
Dimensione 7.7 MB
Formato Adobe PDF
7.7 MB Adobe PDF Visualizza/Apri
RV_2019_ad_hoc_net.pdf

Accesso riservato

Descrizione: Full paper
: Publisher’s version
Dimensione 4.84 MB
Formato Adobe PDF
4.84 MB Adobe PDF   Visualizza/Apri

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/11311/1081014
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 13
  • ???jsp.display-item.citation.isi??? 10
social impact