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An Optimization Scheme for Task Offloading and Resource Allocation in Vehicle Edge Networks

Yuxin Xu1, Zilong Jin1,2,*, Xiaorui Zhang1, Lejun Zhang3
1 School of Computer and Software, Nanjing University of Information Science and Technology, Nanjing, 210044, China
2Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology, Nanjing University of Information Science and Technology, Nanjing, 210044, China
3 College of Information Engineering, Yangzhou University, Yangzhou, 225127, China
* Corresponding Author: Zilong Jin. Email:

Journal on Internet of Things 2020, 2(4), 163-173. https://doi.org/10.32604/jiot.2020.011792

Received 20 May 2020; Accepted 15 August 2020; Issue published 22 September 2020

Abstract

The vehicle edge network (VEN) has become a new research hotspot in the Internet of Things (IOT). However, many new delays are generated during the vehicle offloading the task to the edge server, which will greatly reduce the quality of service (QOS) provided by the vehicle edge network. To solve this problem, this paper proposes an evolutionary algorithm-based (EA) task offloading and resource allocation scheme. First, the delay of offloading task to the edge server is generally defined, then the mathematical model of problem is given. Finally, the objective function is optimized by evolutionary algorithm, and the optimal solution is obtained by iteration and averaging. To verify the performance of this method, contrast experiments are conducted. The experimental results show that our purposed method reduces delay and improves QOS, which is superior to other schemes.

Keywords

Vehicle edge network; evolutionary algorithm; task offloading; resource allocation

Cite This Article

Y. Xu, Z. Jin, X. Zhang and L. Zhang, "An optimization scheme for task offloading and resource allocation in vehicle edge networks," Journal on Internet of Things, vol. 2, no.4, pp. 163–173, 2020.



This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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