4.7 Article

Toward Offloading Internet of Vehicles Applications in 5G Networks

Journal

Publisher

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TITS.2020.3017596

Keywords

Computation offloading; Internet of Vehicles (IoV); fifth-generation (5G) networks; edge computing (EC)

Funding

  1. National Science Foundation of China [61702277, 61772283, 61672276]
  2. Fundamental Research Funds for the Central Universities of China [2722019PY052]
  3. State Key Laboratory for Novel Software Technology, Nanjing University [KFKT2019B17]

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The development of 5G network provides historic opportunities for the EC-IoV system, but also increases the complexity of network connectivity, posing challenges for resource migration and scheduling for edge devices.
The demand for real-time communication and high performance of the Internet of Vehicles (IoV) system has caused researches to investigate new techniques in edge computing (EC). With the rapid development of the fifth- generation (5G) network, the features of low delay, high reliability and superior communication efficiency can bring historic opportunities for the development of the EC-IoV system. In the 5G-enabled EC-IoV system, extreme densification of 5G base stations (gNBs) provides rapid and reliable network access and information interaction. However, this densification also brings more complex connectivity to the network, which increases the difficulty of resource migration and scheduling for the edge devices. Thus, it is still a challenge to manage the resources of the edge devices under the premise of reducing the energy and time cost in the system while avoiding the situation of overload or underload to maintain the stability of the system. In this article, a 5G-enabled EC-IoV system framework is proposed to enhance the performance of *the existing EC-IoV system. Specific computation offloading in 5G-enabled EC-IoV system is presented under three different cases. Through the above cases, two communication modes are concluded and the corresponding resource allocation strategy is given in this article. The performance of the proposed system is evaluated and compared with the existing system. Finally, future research directions in this area are considered.

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