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Time-critical fog computing for vehicular networks

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Time-critical fog computing for vehicular networks. / Chebaane, Ahmed; Khelil, Abdelmajid; Suri, Neeraj.

Fog Computing: Theory and Practice. ed. / Albert Zomaya; Assad Abbas; Samee Khan. London : Wiley, 2020. p. 431-458.

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSNChapter

Harvard

Chebaane, A, Khelil, A & Suri, N 2020, Time-critical fog computing for vehicular networks. in A Zomaya, A Abbas & S Khan (eds), Fog Computing: Theory and Practice. Wiley, London, pp. 431-458. https://doi.org/10.1002/9781119551713.ch17

APA

Chebaane, A., Khelil, A., & Suri, N. (2020). Time-critical fog computing for vehicular networks. In A. Zomaya, A. Abbas, & S. Khan (Eds.), Fog Computing: Theory and Practice (pp. 431-458). Wiley. https://doi.org/10.1002/9781119551713.ch17

Vancouver

Chebaane A, Khelil A, Suri N. Time-critical fog computing for vehicular networks. In Zomaya A, Abbas A, Khan S, editors, Fog Computing: Theory and Practice. London: Wiley. 2020. p. 431-458 https://doi.org/10.1002/9781119551713.ch17

Author

Chebaane, Ahmed ; Khelil, Abdelmajid ; Suri, Neeraj. / Time-critical fog computing for vehicular networks. Fog Computing: Theory and Practice. editor / Albert Zomaya ; Assad Abbas ; Samee Khan. London : Wiley, 2020. pp. 431-458

Bibtex

@inbook{eb916e3514bb41e983fb3f65287b2c03,
title = "Time-critical fog computing for vehicular networks",
abstract = "Moving applications from the local infrastructure to a central data center using cloud computing has been the highlight of the current distributed computing era. This paradigm shift has enabled various new use cases such as computing and storage everywhere and on-demand. A prominent example for cloud computing is off-loading computation and data from smartphones to data centers. The Internet of Things (IoT) aims to connect people and objects such as vehicles, machines, and products with people through the Internet and the cloud. The number of IoT devices significantly has grown in recent years resulting in massive volume of data transferred to the cloud for analysis. Connecting vehicles as a smart thing is required for enhancing the vehicle{\textquoteright}s perception of its surroundings and increasing road safety as well as traffic efficiency. Some applications such as navigation may be delay-tolerant and can be still further distributed among the vehicles and the cloud. Other applications such as collision warnings are delay-critical and accordingly short-lived. For example, detecting and identifying an immediate obstacle on the road requires a quick processing of onboard sensor data (e.g. camera, radar, and LIDAR) in a few tens of milliseconds. With this unprecedented evolution, fog computing has been proposed to support delay-critical computational demand, security issues, communication latency and improve quality of service of vehicular applications. Fog computing is complementary to cloud computing. The interplay of both concepts is now considered as the real enabling architecture for virtually all vehicular networked applications. In this chapter, we first present various scenarios of time-critical applications and their timeliness requirement. We define our application model based on a set of middleware building blocks in order to reach the timeliness guarantees. The key building blocks are resource monitoring, task scheduling, real-time computation, and real-time communication. Then we show how the perturbations can interrupt the communication and the computation between vehicles and/or infrastructures, which is a serious problem in such critical scenarios. Next, we critically review existing research efforts to cope with failures, threats, and constraints of the network, computation, and data management so as to efficiently meet the timeliness requirements despite the perturbation. In particular, we provide a taxonomy of fog computing according to the research topics. Throughout the chapter, we identify research gaps and sketch future research directions.",
keywords = "Cloud computing, Delay-critical application, Fog computing, Heterogeneity, Internet of things, Vehicular networks",
author = "Ahmed Chebaane and Abdelmajid Khelil and Neeraj Suri",
year = "2020",
month = may,
day = "5",
doi = "10.1002/9781119551713.ch17",
language = "English",
isbn = "9781119551690",
pages = "431--458",
editor = "Albert Zomaya and Assad Abbas and Samee Khan",
booktitle = "Fog Computing",
publisher = "Wiley",

}

RIS

TY - CHAP

T1 - Time-critical fog computing for vehicular networks

AU - Chebaane, Ahmed

AU - Khelil, Abdelmajid

AU - Suri, Neeraj

PY - 2020/5/5

Y1 - 2020/5/5

N2 - Moving applications from the local infrastructure to a central data center using cloud computing has been the highlight of the current distributed computing era. This paradigm shift has enabled various new use cases such as computing and storage everywhere and on-demand. A prominent example for cloud computing is off-loading computation and data from smartphones to data centers. The Internet of Things (IoT) aims to connect people and objects such as vehicles, machines, and products with people through the Internet and the cloud. The number of IoT devices significantly has grown in recent years resulting in massive volume of data transferred to the cloud for analysis. Connecting vehicles as a smart thing is required for enhancing the vehicle’s perception of its surroundings and increasing road safety as well as traffic efficiency. Some applications such as navigation may be delay-tolerant and can be still further distributed among the vehicles and the cloud. Other applications such as collision warnings are delay-critical and accordingly short-lived. For example, detecting and identifying an immediate obstacle on the road requires a quick processing of onboard sensor data (e.g. camera, radar, and LIDAR) in a few tens of milliseconds. With this unprecedented evolution, fog computing has been proposed to support delay-critical computational demand, security issues, communication latency and improve quality of service of vehicular applications. Fog computing is complementary to cloud computing. The interplay of both concepts is now considered as the real enabling architecture for virtually all vehicular networked applications. In this chapter, we first present various scenarios of time-critical applications and their timeliness requirement. We define our application model based on a set of middleware building blocks in order to reach the timeliness guarantees. The key building blocks are resource monitoring, task scheduling, real-time computation, and real-time communication. Then we show how the perturbations can interrupt the communication and the computation between vehicles and/or infrastructures, which is a serious problem in such critical scenarios. Next, we critically review existing research efforts to cope with failures, threats, and constraints of the network, computation, and data management so as to efficiently meet the timeliness requirements despite the perturbation. In particular, we provide a taxonomy of fog computing according to the research topics. Throughout the chapter, we identify research gaps and sketch future research directions.

AB - Moving applications from the local infrastructure to a central data center using cloud computing has been the highlight of the current distributed computing era. This paradigm shift has enabled various new use cases such as computing and storage everywhere and on-demand. A prominent example for cloud computing is off-loading computation and data from smartphones to data centers. The Internet of Things (IoT) aims to connect people and objects such as vehicles, machines, and products with people through the Internet and the cloud. The number of IoT devices significantly has grown in recent years resulting in massive volume of data transferred to the cloud for analysis. Connecting vehicles as a smart thing is required for enhancing the vehicle’s perception of its surroundings and increasing road safety as well as traffic efficiency. Some applications such as navigation may be delay-tolerant and can be still further distributed among the vehicles and the cloud. Other applications such as collision warnings are delay-critical and accordingly short-lived. For example, detecting and identifying an immediate obstacle on the road requires a quick processing of onboard sensor data (e.g. camera, radar, and LIDAR) in a few tens of milliseconds. With this unprecedented evolution, fog computing has been proposed to support delay-critical computational demand, security issues, communication latency and improve quality of service of vehicular applications. Fog computing is complementary to cloud computing. The interplay of both concepts is now considered as the real enabling architecture for virtually all vehicular networked applications. In this chapter, we first present various scenarios of time-critical applications and their timeliness requirement. We define our application model based on a set of middleware building blocks in order to reach the timeliness guarantees. The key building blocks are resource monitoring, task scheduling, real-time computation, and real-time communication. Then we show how the perturbations can interrupt the communication and the computation between vehicles and/or infrastructures, which is a serious problem in such critical scenarios. Next, we critically review existing research efforts to cope with failures, threats, and constraints of the network, computation, and data management so as to efficiently meet the timeliness requirements despite the perturbation. In particular, we provide a taxonomy of fog computing according to the research topics. Throughout the chapter, we identify research gaps and sketch future research directions.

KW - Cloud computing

KW - Delay-critical application

KW - Fog computing

KW - Heterogeneity

KW - Internet of things

KW - Vehicular networks

U2 - 10.1002/9781119551713.ch17

DO - 10.1002/9781119551713.ch17

M3 - Chapter

AN - SCOPUS:85102159800

SN - 9781119551690

SP - 431

EP - 458

BT - Fog Computing

A2 - Zomaya, Albert

A2 - Abbas, Assad

A2 - Khan, Samee

PB - Wiley

CY - London

ER -