Stackelberg game‐based task offloading in vehicular edge computing networks

Author(s):  
Shuang Liu ◽  
Jie Tian ◽  
Xiaofang Deng ◽  
Yuan Zhi ◽  
Ji Bian
2021 ◽  
Vol 2021 ◽  
pp. 1-21
Author(s):  
Bo Wang ◽  
Mingchu Li

With the continuous progress of edge computing technology and the development of the Internet of Things technology, scenarios such as smart transportation, smart home, and smart medical care enable people to enjoy the smart era’s convenience. Simultaneously, with the addition of many smart devices, a large number of tasks are submitted to the edge server, making the edge server unable to meet the needs of completing tasks submitted by the smart device. Besides, if the task is submitted to the remote cloud data center, it increases the user’s additional delay and cost. Therefore, it is necessary to improve the task offloading strategy and resource allocation scheme to solve these problems. This paper first proposes a new task offloading mechanism and then proposes a two-stage Stackelberg game model to solve each participant’s interaction problem in the task offloading mechanism and ensure the maximization of their respective interests. Finally, a theoretical analysis proves the equilibrium of the two-stage Stackelberg game. Experiments are used to prove the effectiveness of the proposed mechanism. Comparative experimental results show that the proposed model can achieve better results regarding delay and energy consumption.


2020 ◽  
Vol 69 (5) ◽  
pp. 5549-5561 ◽  
Author(s):  
Shaoyong Guo ◽  
Yao Dai ◽  
Song Guo ◽  
Xuesong Qiu ◽  
Feng Qi

Author(s):  
Lujie Tang ◽  
Bing Tang ◽  
Li Zhang ◽  
Feiyan Guo ◽  
Haiwu He

AbstractTaking the mobile edge computing paradigm as an effective supplement to the vehicular networks can enable vehicles to obtain network resources and computing capability nearby, and meet the current large-scale increase in vehicular service requirements. However, the congestion of wireless networks and insufficient computing resources of edge servers caused by the strong mobility of vehicles and the offloading of a large number of tasks make it difficult to provide users with good quality of service. In existing work, the influence of network access point selection on task execution latency was often not considered. In this paper, a pre-allocation algorithm for vehicle tasks is proposed to solve the problem of service interruption caused by vehicle movement and the limited edge coverage. Then, a system model is utilized to comprehensively consider the vehicle movement characteristics, access point resource utilization, and edge server workloads, so as to characterize the overall latency of vehicle task offloading execution. Furthermore, an adaptive task offloading strategy for automatic and efficient network selection, task offloading decisions in vehicular edge computing is implemented. Experimental results show that the proposed method significantly improves the overall task execution performance and reduces the time overhead of task offloading.


Author(s):  
Naouri Abdenacer ◽  
Hangxing Wu ◽  
Nouri Nabil Abdelkader ◽  
Sahraoui Dhelim ◽  
Huansheng Ning

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