The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
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The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
Les voitures connectées génèrent une énorme quantité d’informations issues des capteurs de l’Internet des objets (IoT), appelées données CAN (Controller Area Network). Récemment, la collecte de données CAN provenant de voitures connectées dans un système cloud suscite un intérêt croissant pour permettre des cas d'utilisation vitaux tels que l'assistance à la conduite sûre. Bien que chaque paquet de données CAN soit très petit, une voiture connectée génère des milliers de paquets de données CAN par seconde. Par conséquent, la collecte de données CAN en temps réel à partir de voitures connectées dans un système cloud est l’un des problèmes les plus difficiles de l’IoT actuel. Dans cet article, nous proposons un service d’élimination de redondance de réseau (EdgeRE) amélioré par Edge computing pour la collecte de données CAN. En développant EdgeRE, nous avons conçu une architecture de compression de données CAN qui combine des ordinateurs embarqués, des centres de données périphériques et un système de cloud public. EdgeRE inclut l'idée de compression hiérarchique des données et de mise en mémoire tampon dynamique des données dans les centres de données périphériques pour la collecte de données CAN en temps réel. À travers une large gamme de tests sur le terrain avec des voitures connectées et un banc d'essai d'informatique de pointe, nous montrons que l'EdgeRE réduit l'utilisation de la bande passante de 88 % et le nombre de paquets de 99 %.
Masahiro YOSHIDA
Chuo University
Koya MORI
NTT
Tomohiro INOUE
NTT
Hiroyuki TANAKA
NTT
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Masahiro YOSHIDA, Koya MORI, Tomohiro INOUE, Hiroyuki TANAKA, "Edge Computing-Enhanced Network Redundancy Elimination for Connected Cars" in IEICE TRANSACTIONS on Communications,
vol. E105-B, no. 11, pp. 1372-1379, November 2022, doi: 10.1587/transcom.2021TMP0003.
Abstract: Connected cars generate a huge amount of Internet of Things (IoT) sensor information called Controller Area Network (CAN) data. Recently, there is growing interest in collecting CAN data from connected cars in a cloud system to enable life-critical use cases such as safe driving support. Although each CAN data packet is very small, a connected car generates thousands of CAN data packets per second. Therefore, real-time CAN data collection from connected cars in a cloud system is one of the most challenging problems in the current IoT. In this paper, we propose an Edge computing-enhanced network Redundancy Elimination service (EdgeRE) for CAN data collection. In developing EdgeRE, we designed a CAN data compression architecture that combines in-vehicle computers, edge datacenters and a public cloud system. EdgeRE includes the idea of hierarchical data compression and dynamic data buffering at edge datacenters for real-time CAN data collection. Across a wide range of field tests with connected cars and an edge computing testbed, we show that the EdgeRE reduces bandwidth usage by 88% and the number of packets by 99%.
URL: https://global.ieice.org/en_transactions/communications/10.1587/transcom.2021TMP0003/_p
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@ARTICLE{e105-b_11_1372,
author={Masahiro YOSHIDA, Koya MORI, Tomohiro INOUE, Hiroyuki TANAKA, },
journal={IEICE TRANSACTIONS on Communications},
title={Edge Computing-Enhanced Network Redundancy Elimination for Connected Cars},
year={2022},
volume={E105-B},
number={11},
pages={1372-1379},
abstract={Connected cars generate a huge amount of Internet of Things (IoT) sensor information called Controller Area Network (CAN) data. Recently, there is growing interest in collecting CAN data from connected cars in a cloud system to enable life-critical use cases such as safe driving support. Although each CAN data packet is very small, a connected car generates thousands of CAN data packets per second. Therefore, real-time CAN data collection from connected cars in a cloud system is one of the most challenging problems in the current IoT. In this paper, we propose an Edge computing-enhanced network Redundancy Elimination service (EdgeRE) for CAN data collection. In developing EdgeRE, we designed a CAN data compression architecture that combines in-vehicle computers, edge datacenters and a public cloud system. EdgeRE includes the idea of hierarchical data compression and dynamic data buffering at edge datacenters for real-time CAN data collection. Across a wide range of field tests with connected cars and an edge computing testbed, we show that the EdgeRE reduces bandwidth usage by 88% and the number of packets by 99%.},
keywords={},
doi={10.1587/transcom.2021TMP0003},
ISSN={1745-1345},
month={November},}
Copier
TY - JOUR
TI - Edge Computing-Enhanced Network Redundancy Elimination for Connected Cars
T2 - IEICE TRANSACTIONS on Communications
SP - 1372
EP - 1379
AU - Masahiro YOSHIDA
AU - Koya MORI
AU - Tomohiro INOUE
AU - Hiroyuki TANAKA
PY - 2022
DO - 10.1587/transcom.2021TMP0003
JO - IEICE TRANSACTIONS on Communications
SN - 1745-1345
VL - E105-B
IS - 11
JA - IEICE TRANSACTIONS on Communications
Y1 - November 2022
AB - Connected cars generate a huge amount of Internet of Things (IoT) sensor information called Controller Area Network (CAN) data. Recently, there is growing interest in collecting CAN data from connected cars in a cloud system to enable life-critical use cases such as safe driving support. Although each CAN data packet is very small, a connected car generates thousands of CAN data packets per second. Therefore, real-time CAN data collection from connected cars in a cloud system is one of the most challenging problems in the current IoT. In this paper, we propose an Edge computing-enhanced network Redundancy Elimination service (EdgeRE) for CAN data collection. In developing EdgeRE, we designed a CAN data compression architecture that combines in-vehicle computers, edge datacenters and a public cloud system. EdgeRE includes the idea of hierarchical data compression and dynamic data buffering at edge datacenters for real-time CAN data collection. Across a wide range of field tests with connected cars and an edge computing testbed, we show that the EdgeRE reduces bandwidth usage by 88% and the number of packets by 99%.
ER -