A Comprehensive Review on Time Sensitive Networks with a Special Focus on Its Applicability to Industrial Smart and Distributed Measurement Systems

Fedullo, Tommaso; Morato, Alberto; Tramarin, Federico; Rovati, Luigi; Vitturi, Stefano

doi:10.3390/s22041638

Cited by 49 publications

(11 citation statements)

References 95 publications

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“…Centralized decisions may be communicated while some sensors are still processing their data. According to standard real-time control [38], [39], [40], we assume what follows.…”

Section: Homogeneous Decisionmentioning

confidence: 99%

A Reinforcement Learning Approach to Sensing Design in Resource-Constrained Wireless Networked Control Systems

Ballotta¹,

Peserico²,

Zanini³

2022

Preprint

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In this paper, we consider a wireless network of smart sensors (agents) that monitor a dynamical process and send measurements to a base station that performs global monitoring and decision-making. Smart sensors are equipped with both sensing and computation, and can either send raw measurements or process them prior to transmission. Constrained agent resources raise a fundamental latency-accuracy trade-off. On the one hand, raw measurements are inaccurate but fast to produce. On the other hand, data processing on resource-constrained platforms generates accurate measurements at the cost of non-negligible computation latency. Further, if processed data are also compressed, latency caused by wireless communication might be higher for raw measurements. Hence, it is challenging to decide when and where sensors in the network should transmit raw measurements or leverage time-consuming local processing. To tackle this design problem, we propose a Reinforcement Learning approach to learn an efficient policy that dynamically decides when measurements are to be processed at each sensor. Effectiveness of our proposed approach is validated through a numerical simulation with case study on smart sensing motivated by the Internet of Drones. This work has been partially funded by the Italian Ministry of Education, University and Research (MIUR) through the PRIN project no. 2017NS9FEY entitled "Realtime Control of 5G Wireless Networks: Taming the Complexity of Future Transmission and Computation Challenges" and through the initiative "Departments of Excellence" (Law 232/2016). The views and opinions expressed in this work are those of the authors and do not necessarily reflect those of the funding institutions.

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“…Centralized decisions may be communicated while some sensors are still processing their data. According to standard real-time control [38], [39], [40], we assume what follows.…”

Section: Homogeneous Decisionmentioning

confidence: 99%

A Reinforcement Learning Approach to Sensing Design in Resource-Constrained Wireless Networked Control Systems

Ballotta¹,

Peserico²,

Zanini³

2022

Preprint

View full text Add to dashboard Cite

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“…For ensuring clock synchronization, time sensitive transmission can be implemented where either the physical broadcast channel (PBCH) or the radio-resource-control (RRC) layer can transmit the high-precision reference time to get accurate synchronization between the terminal clock and the master clock. Since time-sensitivenetworking (TSN) is an IEEE 802.1 Ethernet-based technology, Ethernet frames need to be augmented by headers which can be compressed to get reduced latency and improved data transmission [43].…”

Section: G Integrated With Time-sensitive Networking (Tsn)mentioning

confidence: 99%

State-of-the-Art Strategies and Research Challenges in Wireless Communication for Building Smart Systems

Khuntia¹,

Das²,

Sinha³

et al. 2022

IJWMN

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The upcoming 6G communication network will revolutionize the scenario of customer services and related applications by building smart, autonomous systems. We first discuss here about 6G enabled smart applications like healthcare, smart city building, industrial IoTs, etc. We next provide a brief survey of the enabling technologies for achieving the required goals of 6G, such as terahertz communication, cell-free communication, holographic communication using beamforming, wireless power transfer, ultra-low latency communication, etc., with a view to tackle very large amounts of data traffic from several billions of smart interconnected devices while supporting ultra-low end-to-end communication latency. Finally, we present various research challenges involved in 6G communication towards building such smart systems.

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“…To have proper synchronization among the master and terminal clock, either the Radio-Resource-Control (RRC) layer or the Physical Broadcast CHannel (PBCH) can go for high-precision reference time transmission for ensuring clock synchronization. As TSN is a type of IEEE 802.1 Ethernet-based application, hence the frames of Ethernet are necessarily added with compressed headers for achieving lower latency and better data transmission [21].…”

Section: Integration Of 5g With Time-sensitive Networking (Tsn)mentioning

confidence: 99%

Strategies for Reducing Communication Latency in 6G Networks

Das

Mukherjee

Sinha

2022

Preprint

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Design of computer networks usually aims at optimizing the network performance through efficient network communication protocols to maximize resource utilization and network throughput. Over the different generations of wireless cellular network staring from first (1G) to fifth generation (5G), end-to-end communication latency, bandwidth and energy required for communication have turned out to be the most significant performance parameters to be considered for the design process. Although a significant amount of research has been done in this direction over the last few decades, still lot more attention needs to be given to explore the techniques for improving the end-to-end communication latency specifically in the context of recent 5G and 6G networks. This paper initially presents the expected goals of the 6G networks and the potential smart services to be enabled by 6G. In the later sections, it provides a concise survey of strategies for managing latency-related issues in the literature. Next we focus on the types and sources of delays that affect latency, the enabling technologies for 6G implementation and the potential strategies for reducing latency. Finally, we discuss about some significant challenges and identify a few open research problems to achieve ultra-low end-to-end latency in 6G communication.

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A Comprehensive Review on Time Sensitive Networks with a Special Focus on Its Applicability to Industrial Smart and Distributed Measurement Systems

Cited by 49 publications

References 95 publications

A Reinforcement Learning Approach to Sensing Design in Resource-Constrained Wireless Networked Control Systems

A Reinforcement Learning Approach to Sensing Design in Resource-Constrained Wireless Networked Control Systems

State-of-the-Art Strategies and Research Challenges in Wireless Communication for Building Smart Systems

Strategies for Reducing Communication Latency in 6G Networks

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