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

Title
Adaptive Offloading for Time-Critical Tasks in Heterogeneous Internet of Vehicles
Authors
Liu, ChunhuiGuo, SongtaoXie, RuitaoLee, Victor C. S.Son, Sang HyukLiu, Kai
DGIST Authors
Liu, Chunhui; Guo, Songtao; Xie, Ruitao; Lee, Victor C. S.; Son, Sang Hyuk; Liu, Kai
Issue Date
2020-09
Citation
IEEE Internet of Things Journal, 7(9), 7999-8011
Type
Article
Article Type
Article
Author Keywords
Adaptive offloadingfog computingInternet of Vehicles (IoV)time-critical taskTask analysisTime factorsEdge computingComputer architectureDelaysWireless communicationAdaptation models
ISSN
2327-4662
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.
URI
http://hdl.handle.net/20.500.11750/12834
DOI
10.1109/JIOT.2020.2997720
Publisher
Institute of Electrical and Electronics Engineers Inc.
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