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Wireless Cloud Networks for the Factory of Things: Connectivity Modeling and Layout Design

Articolo
Data di Pubblicazione:
2014
Abstract:
Large-scale adoption of dense cloud-based wireless network technologies in industrial plants is mandatorily paired with the development of methods and tools for connectivity prediction and deployment validation. Layout design procedures must be able to certify the quality (or reliability) of network information flow in industrial scenarios characterized by harsh propagation environments. In addition, these must account for possibly coexisting heterogeneous radio access technologies as part of the internet of things (IoT) paradigm, easily allow postlayout validation steps, and be integrated by industry standard CAD-based planning systems. The goal of the paper is to set the fundamentals for comprehensive industry-standard methods and procedures supporting plant designer during wireless coverage prediction, virtual network deployment and post-layout verification. The proposed methods carry out the prediction of radio signal coverage considering typical industrial environments characterized by highly dense building blockage. They also provide a design framework to properly deploy the wireless infrastructure in interference-limited radio access scenarios. In addition, the model can be effectively used to certify the quality of machine type communication by considering also imperfect descriptions of the network layout. The design procedures are corroborated by experimental measurements in an oil refinery site (modelled by 3D CAD) using industry standard ISA IEC 62734 devices operating at 2.4GHz. A graph-theoretic approach to node deployment is discussed by focusing on practical case studies, and also by looking at fundamental connectivity properties for random deployments.
Tipologia CRIS:
01.01 Articolo in rivista
Keywords:
Factory of Things; Internet of Things; Smart Factory; Wireless Channel Modelling; Industrial Wireless Communication; Machine-type Connnectivity; Network Deployment Optimization
Elenco autori:
Savazzi, Stefano; Rampa, Vittorio
Autori di Ateneo:
SAVAZZI STEFANO
Link alla scheda completa:
https://iris.cnr.it/handle/20.500.14243/249761
Pubblicato in:
IEEE INTERNET OF THINGS JOURNAL
Journal
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http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6777564
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