TY - JOUR
T1 - Integrating NFV and ICN for Advanced Driver-Assistance Systems
AU - Li, Jianan
AU - Wu, Jun
AU - Xu, Guangquan
AU - Li, Jianhua
AU - Zheng, Xi
AU - Jolfaei, Alireza
N1 - Funding Information:
Manuscript received September 25, 2019; revised October 30, 2019; accepted November 11, 2019. Date of publication November 18, 2019; date of current version July 10, 2020. This work was supported in part by the National Natural Science Foundation of China under Grant 61972255, Grant 61572355, and Grant U1736115; and in part by the State Key Development Program of China under Grant 2018YFB0804402. (Corresponding authors: Jun Wu; Guangquan Xu.) J. Li, J. Wu, and J. Li are with the Shanghai Key Laboratory of Integrated Administration Technologies for Information Security, School of Cyber Security, Shanghai Jiao Tong University, Shanghai 200240, China (e-mail: junwuhn@sjtu.edu.cn).
Publisher Copyright:
© 2014 IEEE.
PY - 2020/7
Y1 - 2020/7
N2 - Advanced driver-assistance systems (ADASs) have been proposed as an alternative to driverless vehicles to provide support for automotive vehicle decisions. As a significant driving force for ADASs, the augmented reality (AR) provides comprehensive location-based content services for in-vehicle consumers. With the increase in request for information sharing, the current standalone mode of ADASs needs a shift to the multiuser sharing mode. In this article, to address the high mobility and real time requirements of ADASs in 5G environments, and also to address the resource orchestration and service management of big data in intelligent transportation systems, we integrate the information-centric network (ICN) and the network function virtualization (NFV) with ADASs to support an efficient AR-assisted content sharing and distribution. This integration eliminates the imbalance between the content requests and the resource limitation by splitting the virtual resources and providing an on-demand network and resource slicing in ADASs. We propose an incentive trading model for assistance content caching services and also propose a novel mechanism for optimal content cache allocation. Our extensive evaluation confirms that our proposed mechanism outperforms the past literature in terms of the cache hit ratio and latency.
AB - Advanced driver-assistance systems (ADASs) have been proposed as an alternative to driverless vehicles to provide support for automotive vehicle decisions. As a significant driving force for ADASs, the augmented reality (AR) provides comprehensive location-based content services for in-vehicle consumers. With the increase in request for information sharing, the current standalone mode of ADASs needs a shift to the multiuser sharing mode. In this article, to address the high mobility and real time requirements of ADASs in 5G environments, and also to address the resource orchestration and service management of big data in intelligent transportation systems, we integrate the information-centric network (ICN) and the network function virtualization (NFV) with ADASs to support an efficient AR-assisted content sharing and distribution. This integration eliminates the imbalance between the content requests and the resource limitation by splitting the virtual resources and providing an on-demand network and resource slicing in ADASs. We propose an incentive trading model for assistance content caching services and also propose a novel mechanism for optimal content cache allocation. Our extensive evaluation confirms that our proposed mechanism outperforms the past literature in terms of the cache hit ratio and latency.
KW - Augmented reality (AR)
KW - Internet of Vehicles (IoV)
KW - driver assistance
KW - information-centric network (ICN)
KW - network function virtualization (NFV)
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U2 - 10.1109/JIOT.2019.2953988
DO - 10.1109/JIOT.2019.2953988
M3 - Article
AN - SCOPUS:85085248978
SN - 2327-4662
VL - 7
SP - 5861
EP - 5873
JO - IEEE Internet of Things Journal
JF - IEEE Internet of Things Journal
IS - 7
M1 - 8903315
ER -