Adaptive Offloading for Time-Critical Tasks in Heterogeneous Internet of Vehicles

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

2 Scopus Citations
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Author(s)

  • Chunhui Liu
  • Kai Liu
  • Songtao Guo
  • Ruitao Xie
  • Sang H. Son

Related Research Unit(s)

Detail(s)

Original languageEnglish
Article number9099808
Pages (from-to)7999-8011
Journal / PublicationIEEE Internet of Things Journal
Volume7
Issue number9
Online published26 May 2020
Publication statusPublished - Sep 2020

Abstract

With the recent development of wireless communication, sensing, and computing technologies, Internet of Vehicles (IoV) has attracted great attention in both academia and industry. Nevertheless, it is challenging to process time-critical tasks due to unique characteristics of IoV, including heterogeneous computation and communication capacities of network nodes, intermittent wireless connections, unevenly distributed workload, massive data transmission, intensive computation demands, and high mobility of vehicles. In this article, we propose a two-layer vehicular fog computing (VFC) architecture to explore the synergistic effect of the cloud, the static fog, and the mobile fog on processing time-critical tasks in IoV. Then, we give a motivational case study by implementing a prototype of a traffic abnormity detection and warning system, which demonstrates the necessity and urgency of developing adaptive task offloading mechanisms in such a scenario and gives insight into the problem formulation. Furthermore, we formulate the offloading model, aiming at maximizing the completion ratio of time-critical tasks. On this basis, we propose an adaptive task offloading algorithm (ATOA). Specifically, it adaptively categorizes all tasks into four types of pending lists by considering the dynamic requirements and resource constraints, and then tasks in each list will be cooperatively offloaded to different nodes based on their features. Finally, we build the simulation model and give a comprehensive performance evaluation. The results demonstrate the superiority of ATOA.

Research Area(s)

  • Adaptive offloading, fog computing, Internet of Vehicles (IoV), time-critical task

Citation Format(s)

Adaptive Offloading for Time-Critical Tasks in Heterogeneous Internet of Vehicles. / Liu, Chunhui; Liu, Kai; Guo, Songtao; Xie, Ruitao; Lee, Victor C. S.; Son, Sang H.

In: IEEE Internet of Things Journal, Vol. 7, No. 9, 9099808, 09.2020, p. 7999-8011.

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review