Network Slicing in Industry 4.0 Applications: Abstraction Methods and End-to-End Analysis

Kalør, Anders E.; Guillaume, René; Nielsen, Jimmy Jessen; Mueller, A.; Popovski, Petar

doi:10.1109/tii.2018.2839721

Cited by 87 publications

(61 citation statements)

References 25 publications

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“…Additionally, different IIoT deployments usually incorporate different communication and networking alternatives, such as WIrelessHART [105], RPL [126] and 6TiSCH [106], as well as frequent protocol conversions [103], operations which have to seamlessly exchange data with each other. Consequently IIoT and ICPS technologies enable intelligent, adaptive control with seamless vertical, horizontal and dynamic data exchange between heterogeneous platforms and networks, through an exhaustive use of data exchange, coordination and collaboration [119], as well as through recently proposed techniques like network slicing [114]. Important ICPS operations include fault management [121], clustering analytics [122], reusable software [123], as well as reactive test case generation [124] and modular reconfiguration [125].…”

Section: B Architectures Focusing On Iiot / Icps and Wsanmentioning

confidence: 99%

Data Management in Industry 4.0: State of the Art and Open Challenges

2019

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Information and communication technologies are permeating all aspects of industrial and manufacturing systems, expediting the generation of large volumes of industrial data. This article surveys the recent literature on data management as it applies to networked industrial environments and identifies several open research challenges for the future. As a first step, we extract important data properties (volume, variety, traffic, criticality) and identify the corresponding data enabling technologies of diverse fundamental industrial use cases, based on practical applications. Secondly, we provide a detailed outline of recent industrial architectural designs with respect to their data management philosophy (data presence, data coordination, data computation) and the extent of their distributiveness. Then, we conduct a holistic survey of the recent literature from which we derive a taxonomy of the latest advances on industrial data enabling technologies and data centric services, spanning all the way from the field level deep in the physical deployments, up to the cloud and applications level. Finally, motivated by the rich conclusions of this critical analysis, we identify interesting open challenges for future research. The concepts presented in this article thematically cover the largest part of the industrial automation pyramid layers. Our approach is multidisciplinary, as the selected publications were drawn from two fields; the communications, networking and computation field as well as the industrial, manufacturing and automation field. The article can help the readers to deeply understand how data management is currently applied in networked industrial environments, and select interesting open research opportunities to pursue.

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Section: B Architectures Focusing On Iiot / Icps and Wsanmentioning

confidence: 99%

Data Management in Industry 4.0: State of the Art and Open Challenges

2019

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“…However, network slicing impact on reliability and latency performance has been limited, and improving reliability methods has not been investigated on s-health [ 10 ]. Generally, studies on network slicing implement some network slice type, such as URLLC, following some priority queuing [ 17 , 29 , 30 , 31 ], or present an end-to-end architecture [ 19 , 37 ].…”

Section: Performance Management Approachesmentioning

confidence: 99%

“…The authors created three URLLC slices to control vehicle-state reports (e.g., speed, and location), event-drive (e.g., emergency), and user information. Kalor et al [ 29 ] used network slicing with priority queuing to manage the different requirements, such as reliability, low latency, and high data rates. Considering a personalized medicine manufacturing use case, the authors created three levels: static telegram allocation (traffic from one application), shared telegram (traffic from several applications), and telegram overwriting (applications with higher priority overwrite network traffic).…”

Section: Performance Management Approachesmentioning

confidence: 99%

“…Based on the literature, there are several open issues related to network slicing since it is a novel concept. Several studies addressed the URLLC slice type, which involves critical applications with critical requirements, e.g., remote surgery and autonomous vehicles [ 17 , 29 , 31 ]. However, the studies addressed an individual characteristic of the network slicing concept, i.e., some studies addressed queuing priority.…”

Section: Performance Management Approachesmentioning

confidence: 99%

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An Architecture for the Performance Management of Smart Healthcare Applications

Vergütz

Prates

Schwengber

et al. 2020

Sensors

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The sixth-generation (6G) network intends to revolutionize the healthcare sector. It will offer smart healthcare (s-health) treatments and allow efficient patient remote monitoring, exposing the high potential of 6G communication technology in telesurgery, epidemic, and pandemic. Healthcare relies on 6G communication technology, diminishing barriers as time and space. S-health applications require strict network requirements, for instance, 99.999% of service reliability and 1 ms of end-to-end latency. However, it is a challenging task to manage network resources and applications towards such performance requirements. Hence, significant attention focuses on performance management as a way of searching for efficient approaches to adjust and tune network resources to application needs, assisting in achieving the required performance levels. In the literature, performance management employs techniques such as resource allocation, resource reservation, traffic shaping, and traffic scheduling. However, they are dedicated to specific problems such as resource allocation for a particular device, ignoring the heterogeneity of network devices, and communication technology. Thus, this article presents PRIMUS, a performance management architecture that aims to meet the requirements of low-latency and high-reliability in an adaptive way for s-health applications. As network slicing is central to realizing the potential of 5G–6G networks, PRIMUS manages traffic through network slicing technologies. Unlike existing proposals, it supports device and service heterogeneity based on the autonomous knowledge of s-health applications. Emulation results in Mininet-WiFi show the feasibility of meeting the s-health application requirements in virtualized environments.

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“…Moreover, we tackle the second question by proposing a novel cloud-edge functional split for the coexistence of URLLC and eMBB traffic. Accordingly, URLLC traffic is handled at the ENs in order to meet the low-latency requirements, URLLC devices may be vehicles in vehicle-tocellular use cases [13], or they may be devices serving automation chains in Industry 4.0 scenarios characterized by automation and communication-based manufacturing [14] [15]. In contrast, eMBB traffic is processed by the centralized BaseBand Unit (BBU) in the cloud as in a C-RAN architecture in order to enhance spectral efficiency thanks to the cloud's interference management capabilities [16].…”

Section: A Main Contributionsmentioning

confidence: 99%

Non-Orthogonal Multiplexing of Ultra-Reliable and Broadband Services in Fog-Radio Architectures

et al. 2019

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The fifth generation (5G) of cellular systems is introducing Ultra-Reliable Low-Latency Communications (URLLC) services alongside more conventional enhanced Mobile BroadBand (eMBB) traffic. Furthermore, the 5G cellular architecture is evolving from a base station-centric deployment to a foglike set-up that accommodates a flexible functional split between cloud and edge. In this paper, a novel solution is proposed that enables the non-orthogonal coexistence of URLLC and eMBB services by processing URLLC traffic at the Edge Nodes (ENs), while eMBB communications are handled centrally at a cloud processor as in a Cloud-Radio Access Network (C-RAN) system. This solution guarantees the low-latency requirements of the URLLC service by means of edge processing, e.g., for vehicle-to-cellular use cases, as well as the high spectral efficiency for eMBB traffic via centralized baseband processing.Both uplink and downlink are analyzed by accounting for the heterogeneous performance requirements of eMBB and URLLC traffic and by considering practical aspects such as fading, lack of channel state information for URLLC transmitters, rate adaptation for eMBB transmitters, finite fronthaul capacity, and different coexistence strategies, such as puncturing.

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Network Slicing in Industry 4.0 Applications: Abstraction Methods and End-to-End Analysis

Cited by 87 publications

References 25 publications

Data Management in Industry 4.0: State of the Art and Open Challenges

Data Management in Industry 4.0: State of the Art and Open Challenges

An Architecture for the Performance Management of Smart Healthcare Applications

Non-Orthogonal Multiplexing of Ultra-Reliable and Broadband Services in Fog-Radio Architectures

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