IETF 6TiSCH: A Tutorial

Vilajosana, Xavier; Watteyne, Thomas; Chang, Tengfei; Vučinić, Mališa; Duquennoy, Simon; Thubert, Pascal

doi:10.1109/comst.2019.2939407

Cited by 158 publications

(100 citation statements)

References 57 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…The IETF “IPv6 over the TSCH mode of IEEE802.15.4e” 6TiSCH working group has standardized how to combine TSCH and IPv6 [ 28 ]. The 6TiSCH protocol stack is rooted in IEEE802.15.4 at the PHY, and the TSCH mode of IEEE802.15.4e at the link layer.…”

Section: A Phy-layer Agile Extension Of Openwsnmentioning

confidence: 99%

No Free Lunch—Characterizing the Performance of 6TiSCH When Using Different Physical Layers

Rady

Lampin

Barthel

et al. 2020

Sensors

Self Cite

View full text Add to dashboard Cite

Low-power wireless applications require different trade-off points between latency, reliability, data rate and power consumption. Given such a set of constraints, which physical layer should I be using? We study this question in the context of 6TiSCH, a state-of-the-art recently standardized protocol stack developed for harsh industrial applications. Specifically, we augment OpenWSN, the reference 6TiSCH open-source implementation, to support one of three physical layers from the IEEE802.15.4g standard: FSK 868 MHz which offers long range, OFDM 868 MHz which offers high data rate, and O-QPSK 2.4 GHz which offers more balanced performance. We run the resulting firmware on the 42-mote OpenTestbed deployed in an office environment, once for each physical layer. Performance results show that, indeed, no physical layer outperforms the other for all metrics. This article argues for combining the physical layers, rather than choosing one, in a generalized 6TiSCH architecture in which technology-agile radio chips (of which there are now many) are driven by a protocol stack which chooses the most appropriate physical layer on a frame-by-frame basis.

show abstract

Section: A Phy-layer Agile Extension Of Openwsnmentioning

confidence: 99%

No Free Lunch—Characterizing the Performance of 6TiSCH When Using Different Physical Layers

Rady

Lampin

Barthel

et al. 2020

Sensors

Self Cite

View full text Add to dashboard Cite

show abstract

“…The 6TiSCH has also defined secure light-weight join processes combining link-layer security features with a secure joining procedure using CoAP [37]. All these characteristics, particularly high reliability, deterministic latency, and ultra-low operations, make a 6TiSCH suitable for numerous healthcare applications.…”

Section: Advances In Lr-wpan Networkingmentioning

confidence: 99%

Emerging Wireless Sensor Networks and Internet of Things Technologies—Foundations of Smart Healthcare

Gardasevic

Katzis

Bajić

et al. 2020

Sensors

View full text Add to dashboard Cite

Future smart healthcare systems—often referred to as Internet of Medical Things (IoMT) – will combine a plethora of wireless devices and applications that use wireless communication technologies to enable the exchange of healthcare data. Smart healthcare requires sufficient bandwidth, reliable and secure communication links, energy-efficient operations, and Quality of Service (QoS) support. The integration of Internet of Things (IoT) solutions into healthcare systems can significantly increase intelligence, flexibility, and interoperability. This work provides an extensive survey on emerging IoT communication standards and technologies suitable for smart healthcare applications. A particular emphasis has been given to low-power wireless technologies as a key enabler for energy-efficient IoT-based healthcare systems. Major challenges in privacy and security are also discussed. A particular attention is devoted to crowdsourcing/crowdsensing, envisaged as tools for the rapid collection of massive quantities of medical data. Finally, open research challenges and future perspectives of IoMT are presented.

show abstract

“…Considering the IEEE 802.15.4e physical layer, for links whose SNR is above the sensitivity threshold β = −90 dBm, the standard supports frames of up to 127 bytes ( B W = 0.08 bit/s/Hz), and Transmission Time Intervals (TTI) equal to TTI = 4 ms, leaving about B = 100 bytes [18] as payload to be used by FL. Considering such small frame size, pruning and quantization of model parameters is fundamental.…”

Section: B Model Pruning and Communication Of Model Parametersmentioning

confidence: 99%

A Joint Decentralized Federated Learning and Communications Framework for Industrial Networks

Savazzi

Kianoush

Rampa

et al. 2020

2020 IEEE 25th International Workshop on Computer Aided Modeling and Design of Communication Links and Networks (CAMAD)

View full text Add to dashboard Cite

Industrial wireless networks are pushing towards distributed architectures moving beyond traditional server-client transactions. Paired with this trend, new synergies are emerging among sensing, communications and Machine Learning (ML) codesign, where resources need to be distributed across different wireless field devices, acting as both data producers and learners. Considering this landscape, Federated Learning (FL) solutions are suitable for training a ML model in distributed systems. In particular, decentralized FL policies target scenarios where learning operations must be implemented collaboratively, without relying on the server, and by exchanging model parameters updates rather than training data over capacity-constrained radio links. This paper proposes a real-time framework for the analysis of decentralized FL systems running on top of industrial wireless networks rooted in the popular Time Slotted Channel Hopping (TSCH) radio interface of the IEEE 802.15.4e standard. The proposed framework is suitable for neural networks trained via distributed Stochastic Gradient Descent (SGD), it quantifies the effects of model pruning, sparsification and quantization, as well as physical and link layer constraints, on FL convergence time and learning loss. The goal is to set the fundamentals for comprehensive methods and procedures supporting decentralized FL pre-deployment design. The proposed tool can be thus used to optimize the deployment of the wireless network and the ML model before its actual installation. It has been verified based on real data targeting smart robotic-assisted manufacturing. 12 ÷ 16 7 ÷ 14 13 ÷ 16 6 ÷ 14 Gossip (ϱ = 10 −3) Gossip (ϱ = 10 −4) Consensus (ϱ = 10 −3) Consensus (ϱ = 10 −4) 20 ÷ 35 18 s. 63 s. 27 s. 95 s. 5 ÷ 12 0.8 ÷ 3 4 ÷ 10 0.25 ÷ 0.7 Gossip (ϱ = 10 −3) Gossip (ϱ = 10 −4) Consensus (ϱ = 10 −3) Consensus (ϱ = 10 −4) 40 ÷ 60 8 s. 41 s. 12 s. 53 s. 0.15 ÷ 0.35 0.1 ÷ 0.2 0.1 ÷ 0.2 0.07 ÷ 0.13 Gossip (ϱ = 10 −3) Gossip (ϱ = 10 −4) Consensus (ϱ = 10 −3) Consensus (ϱ = 10 −4

show abstract

IETF 6TiSCH: A Tutorial

Cited by 158 publications

References 57 publications

No Free Lunch—Characterizing the Performance of 6TiSCH When Using Different Physical Layers

No Free Lunch—Characterizing the Performance of 6TiSCH When Using Different Physical Layers

Emerging Wireless Sensor Networks and Internet of Things Technologies—Foundations of Smart Healthcare

A Joint Decentralized Federated Learning and Communications Framework for Industrial Networks

Contact Info

Product

Resources

About