QoS-enabled resource allocation algorithm in internet of vehicles with mobile edge computing

Along with the development of 5G technology in mobile sensing and wireless communication, the internet of vehicles (IoV) has drawn much attention from the research community. The traditional centralised cloud-based IoV network has become a bottleneck in providing computation-intensive, high-mobility...

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Veröffentlicht in:IET communications Jg. 14; H. 14; S. 2326 - 2333
Hauptverfasser: Wang, Ge, Xu, Fangmin, Zhao, Chenglin
Format: Journal Article
Sprache:Englisch
Veröffentlicht: The Institution of Engineering and Technology 25.08.2020
Schlagworte:
5G mobile communication
account different delay tolerances
cloud computing
different task types
hierarchical IoV system
high‐mobility
Internet
learning (artificial intelligence)
low‐latency services
MEC
mobile edge computing
mobile sensing
probability
promising computing paradigm
QoS‐enabled resource allocation algorithm
quality of service
Research Article
resource allocation
service‐enabled resource allocation
traditional centralised cloud‐based IoV network
wireless communication
5G mobile communication
probability
traditional centralised cloud-based IoV network
low-latency services
MEC
quality of service
hierarchical IoV system
promising computing paradigm
wireless communication
account different delay tolerances
QoS-enabled resource allocation algorithm
resource allocation
service-enabled resource allocation
mobile sensing
high-mobility
mobile edge computing
Internet
learning (artificial intelligence)
cloud computing
different task types
ISSN:1751-8628, 1751-8636
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Beschreibung
Zusammenfassung:Along with the development of 5G technology in mobile sensing and wireless communication, the internet of vehicles (IoV) has drawn much attention from the research community. The traditional centralised cloud-based IoV network has become a bottleneck in providing computation-intensive, high-mobility and low-latency services. As a promising computing paradigm, mobile edge computing (MEC) addresses such challenges. In this study, the authors propose a hierarchical IoV system, combined with MEC. They then focus on the problem of quality of service (QoS)-enabled resource allocation for computing tasks in the system. However, the existing studies often fail to take into account different delay tolerances between different task types. In order to optimise the completion delay, they design an approach to classify tasks into different priorities according to their delay tolerances and then reorder tasks. After reordering, they use a reinforcement learning algorithm to allocate resources automatically and intelligently. Simulation results confirm that the proposed scheme is feasible and effective in the aspects of time efficiency and outage probability.
ISSN:1751-8628
1751-8636
DOI:10.1049/iet-com.2019.0981