IEICE TRANSACTIONS on Communications
Edge Computing-Enhanced Network Redundancy Elimination for Connected Cars
Summary :
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%.
- Publication
- IEICE TRANSACTIONS on Communications Vol.E105-B No.11 pp.1372-1379
- Publication Date
- 2022/11/01
- Publicized
- 2022/05/27
- Online ISSN
- 1745-1345
- DOI
- 10.1587/transcom.2021TMP0003
- Type of Manuscript
- Special Section PAPER (Special Section on Towards Management for Future Communications and Services in Conjunction with Main Topics of APNOMS2021)
- Category
Authors
Masahiro YOSHIDA
Chuo University
Koya MORI
NTT
Tomohiro INOUE
NTT
Hiroyuki TANAKA
NTT
Keyword
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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},}
Copy
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 -
English
