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Caching-Aided Collaborative D2D Operation for Predictive Data Dissemination in Industrial IoT

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Caching-Aided Collaborative D2D Operation for Predictive Data Dissemination in Industrial IoT. / Orsino, Antonino; Kovalchukov, Roman; Samuylov, Andrey; Moltchanov, Dmitri; Andreev, Sergey; Koucheryavy, Yevgeni; Valkama, Mikko.

In: IEEE Wireless Communications, Vol. 25, No. 3, 01.06.2018, p. 50-57.

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Orsino, Antonino ; Kovalchukov, Roman ; Samuylov, Andrey ; Moltchanov, Dmitri ; Andreev, Sergey ; Koucheryavy, Yevgeni ; Valkama, Mikko. / Caching-Aided Collaborative D2D Operation for Predictive Data Dissemination in Industrial IoT. In: IEEE Wireless Communications. 2018 ; Vol. 25, No. 3. pp. 50-57.

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@article{22d18ce48d1442eb8858516722da48be,
title = "Caching-Aided Collaborative D2D Operation for Predictive Data Dissemination in Industrial IoT",
abstract = "Industrial automation deployments constitute challenging environments where moving IoT machines may produce high-definition video and other heavy sensor data during surveying and inspection operations. Transporting massive contents to the edge network infrastructure and then eventually to the remote human operator requires reliable and high-rate radio links supported by intelligent data caching and delivery mechanisms. In this work, we address the challenges of contents dissemination in characteristic factory automation scenarios by proposing to engage moving industrial machines as D2D caching helpers. With the goal of improving the reliability of high-rate mmWave data connections, we introduce alternative contents dissemination modes and then construct a novel mobility-aware methodology that helps develop predictive mode selection strategies based on the anticipated radio link conditions. We also conduct a thorough system-level evaluation of representative data dissemination strategies to confirm the benefits of predictive solutions that employ D2D-enabled collaborative caching at the wireless edge to lower contents delivery latency and improve data acquisition reliability.",
author = "Antonino Orsino and Roman Kovalchukov and Andrey Samuylov and Dmitri Moltchanov and Sergey Andreev and Yevgeni Koucheryavy and Mikko Valkama",
year = "2018",
month = "6",
day = "1",
doi = "10.1109/MWC.2018.1700320",
language = "English",
volume = "25",
pages = "50--57",
journal = "IEEE Wireless Communications",
issn = "1536-1284",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
number = "3",

}

RIS (suitable for import to EndNote) - Download

TY - JOUR

T1 - Caching-Aided Collaborative D2D Operation for Predictive Data Dissemination in Industrial IoT

AU - Orsino, Antonino

AU - Kovalchukov, Roman

AU - Samuylov, Andrey

AU - Moltchanov, Dmitri

AU - Andreev, Sergey

AU - Koucheryavy, Yevgeni

AU - Valkama, Mikko

PY - 2018/6/1

Y1 - 2018/6/1

N2 - Industrial automation deployments constitute challenging environments where moving IoT machines may produce high-definition video and other heavy sensor data during surveying and inspection operations. Transporting massive contents to the edge network infrastructure and then eventually to the remote human operator requires reliable and high-rate radio links supported by intelligent data caching and delivery mechanisms. In this work, we address the challenges of contents dissemination in characteristic factory automation scenarios by proposing to engage moving industrial machines as D2D caching helpers. With the goal of improving the reliability of high-rate mmWave data connections, we introduce alternative contents dissemination modes and then construct a novel mobility-aware methodology that helps develop predictive mode selection strategies based on the anticipated radio link conditions. We also conduct a thorough system-level evaluation of representative data dissemination strategies to confirm the benefits of predictive solutions that employ D2D-enabled collaborative caching at the wireless edge to lower contents delivery latency and improve data acquisition reliability.

AB - Industrial automation deployments constitute challenging environments where moving IoT machines may produce high-definition video and other heavy sensor data during surveying and inspection operations. Transporting massive contents to the edge network infrastructure and then eventually to the remote human operator requires reliable and high-rate radio links supported by intelligent data caching and delivery mechanisms. In this work, we address the challenges of contents dissemination in characteristic factory automation scenarios by proposing to engage moving industrial machines as D2D caching helpers. With the goal of improving the reliability of high-rate mmWave data connections, we introduce alternative contents dissemination modes and then construct a novel mobility-aware methodology that helps develop predictive mode selection strategies based on the anticipated radio link conditions. We also conduct a thorough system-level evaluation of representative data dissemination strategies to confirm the benefits of predictive solutions that employ D2D-enabled collaborative caching at the wireless edge to lower contents delivery latency and improve data acquisition reliability.

U2 - 10.1109/MWC.2018.1700320

DO - 10.1109/MWC.2018.1700320

M3 - Article

VL - 25

SP - 50

EP - 57

JO - IEEE Wireless Communications

JF - IEEE Wireless Communications

SN - 1536-1284

IS - 3

ER -