Cross-Layer Pacing for Predictably Low Latency

Schmidt, Andreas; Pereira, Pablo Gil; Hönig, Timo; Herfet, Thorsten; ̈oder-Preikschat, Wolfgang Schr

doi:10.1109/infocomwkshps47286.2019.9093750

Cited by 8 publications

(4 citation statements)

References 12 publications

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“…On the mobile node, the interaction aspect between communication layers (e.g., application and media access control (MAC) layer) concerns the placement of certain functions (e.g., retransmissions) within the entire software hierarchy [37] such that energy-efficient operation is not affected by unnecessary functional redundancies. Besides, cross-layer designs that optimize the timing of communication processes and make them deterministic at least within limits [38] form the softwareengineering basis for an overall energy-aware system approach. In our scenario, energy-efficient and reliable communication between nodes are cross-cutting concerns, because failed communication attempts lead to additional overhead for retransmissions.…”

Section: Discussionmentioning

confidence: 99%

Thinking small: Next-generation sensor networks close the size gap in vertebrate biologging

et al. 2020

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Recent advances in animal tracking technology have ushered in a new era in biologging. However, the considerable size of many sophisticated biologging devices restricts their application to larger animals, whereas older techniques often still represent the state-of-theart for studying small vertebrates. In industrial applications, low-power wireless sensor networks (WSNs) fulfill requirements similar to those needed to monitor animal behavior at high resolution and at low tag mass. We developed a wireless biologging network (WBN), which enables simultaneous direct proximity sensing, high-resolution tracking, and long-range remote data download at tag masses of 1 to 2 g. Deployments to study wild bats created social networks and flight trajectories of unprecedented quality. Our developments highlight the vast capabilities of WBNs and their potential to close an important gap in biologging: fully automated tracking and proximity sensing of small animals, even in closed habitats, at high spatial and temporal resolution.

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Section: Discussionmentioning

confidence: 99%

Thinking small: Next-generation sensor networks close the size gap in vertebrate biologging

et al. 2020

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“…D RS is the response delay of the system and models operating system delays-e.g., packet management or scheduling. Although a more precise adaptation can be achieved by feeding dynamic response delays into the algorithm [49], we have opted for a rather conservative constant value (D RS = 1 ms) to reduce the dimensions of the input dataset-see Sec. 6.1.…”

Section: Parity Packetmentioning

confidence: 99%

“…DeepSHARQ demands from the transport protocol some mechanism to accurately sense the channel-e.g., delivery rate estimation such as in BBR [9] for R C and RT T , and some other mechanism for the error rate [21,26,27]-, and an interface to the application to state its requirements [21,49]. Once this information is known, the pipeline depicted in Fig.…”

Section: System Architecturementioning

confidence: 99%

DeepSHARQ: Hybrid Error Coding using Deep Learning

Pereira¹,

Vogelgesang²,

Miodek³

et al. 2023

Preprint

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Purpose: Cyber-Physical Systems operate under changing environments and on resource-constrained devices. Communication in these environments must use hybrid error coding, as pure pro- or reactiveschemes cannot always fulfill application demands or have suboptimal performance. However, findingoptimal coding configurations that fulfill application constraints—e.g., tolerate loss and delay—underchanging channel conditions is a computationally challenging task. Methods: Recently, the systems community has started addressing these sorts of problems usinghybrid decomposed solutions, i.e., algorithmic approaches for well-understood formalized parts ofthe problem and learning-based approaches for parts that must be estimated (either for reasons ofuncertainty or computational intractability). For DeepSHARQ, we revisit our own recent work andlimit the learning problem to block length prediction, the major contributor to inference time (andits variation) when searching for hybrid error coding configurations. The remaining parameters arefound algorithmically, and hence we make individual contributions with respect to finding close-to-optimal coding configurations in both of these areas—combining them into a hybrid solution. Results: DeepSHARQ applies block length regularization in order to reduce the neural networksin comparison to purely learning-based solutions. The hybrid solution is nearly optimal concerningthe channel efficiency of coding configurations it generates, as it is trained so deviations from theoptimum are upper-bound by a configurable percentage. Additionally, DeepSHARQ is capable ofreacting to channel changes in real-time, thereby enabling Cyber-Physical Systems even on resource-constrained platforms. Conclusion: Tightly integrating algorithmic and learning-based approaches allows DeepSHARQ toreact to channel changes faster and with a more predictable time than solutions that rely only oneither of the two approaches.

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“…In order to guarantee a successful operation, CPS require predictable reliability and delay, which are difficult to achieve due to the inherent design of computation and communication systems [1]. The Predictably Reliable Real-time Transport (PRRT) [2] protocol can optimize its configuration to meet the delay constraints of a system with cross-layer pacing [3], which keeps the system and network buffers empty to minimize the end-to-end delay. Timely reliability is achieved with a Hybrid ARQ (HARQ) scheme that extends the error correction in lower layers to cope with losses not bound to the link within a target delay.…”

Section: Introductionmentioning

confidence: 99%

Reducing FEC-Complexity in Cross-Layer Predictable Data Communication

Pereira

Herfet

2021

2021 IEEE 18th Annual Consumer Communications &Amp; Networking Conference (CCNC)

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The PRRT protocol enables applications with strict performance requirements such as Cyber-Physical Systems, as it provides predictably low, end-to-end delay via cross-layer pacing and timely error correction via Hybrid ARQ (HARQ). However, the implemented HARQ uses computationally complex Maximum Distance Separable (MDS) codes to generate redundancy. In this paper we propose code partitioning for the complexity reduction of MDS codes, thereby enabling their deployment on constrained embedded devices.

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Cross-Layer Pacing for Predictably Low Latency

Cited by 8 publications

References 12 publications

Thinking small: Next-generation sensor networks close the size gap in vertebrate biologging

Thinking small: Next-generation sensor networks close the size gap in vertebrate biologging

DeepSHARQ: Hybrid Error Coding using Deep Learning

Reducing FEC-Complexity in Cross-Layer Predictable Data Communication

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