Three-stage Stackelberg game based edge computing resource management for mobile blockchain

Author(s):  
Yuqi Fan ◽  
Zhifeng Jin ◽  
Guangming Shen ◽  
Donghui Hu ◽  
Lei Shi ◽  
...  
Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Guang-Shun Li ◽  
Ying Zhang ◽  
Mao-Li Wang ◽  
Jun-Hua Wu ◽  
Qing-Yan Lin ◽  
...  

With the emergence and development of the Internet of Vehicles (IoV), quick response time and ultralow delay are required. Cloud computing services are unfavorable for reducing delay and response time. Mobile edge computing (MEC) is a promising solution to address this problem. In this paper, we combined MEC and IoV to propose a specific vehicle edge resource management framework, which consists of fog nodes (FNs), data service agents (DSAs), and cars. A dynamic service area partitioning algorithm is designed to balance the load of DSA and improve the quality of service. A resource allocation framework based on the Stackelberg game model is proposed to analyze the pricing problem of FNs and the data resource strategy of DSA with a distributed iteration algorithm. The simulation results show that the proposed framework can ensure the allocation efficiency of FN resources among the cars. The framework achieves the optimal strategy of the participants and subgame perfect Nash equilibrium.


2019 ◽  
Vol 15 (7) ◽  
pp. 155014771986155 ◽  
Author(s):  
Shaoyong Guo ◽  
Xing Hu ◽  
Gangsong Dong ◽  
Wencui Li ◽  
Xuesong Qiu

Mobile edge computing has attracted great interests in the popularity of fifth-generation (5G) networks and Internet of Things. It aims to supply low-latency and high-interaction services for delay-sensitive applications. Utilizing mobile edge computing with Smart Home, which is one of the most important fields of Internet of Things, is a method to satisfy users’ demand for higher computing power and storage capacity. However, due to limited computing resource, how to improve efficiency of resource allocation is a challenge. In this article, we propose a hierarchical architecture in Smart Home with mobile edge computing, providing low-latency services and promoting edge process for smart devices. Based on that, a Stackelberg Game is designed in order to allocate computing resource to devices efficiently. Then, one-to-many matching is established to handle resource allocation problems. It is proved that the allocation strategy can optimize the utility of mobile edge computing server and improve allocating efficiency. Simulation results show the effectiveness of the proposed strategy compared with schemes based on auction game, and present performance with different changing system parameters.


2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Xintao Wu ◽  
Jie Gan ◽  
Shiyong Chen ◽  
Xu Zhao ◽  
Yucheng Wu

Mobile edge computing (MEC) provides user equipment (UE) with computing capability through wireless networks to improve the quality of experience (QoE). The scenario with multiple base stations and multiple mobile users is modeled and analyzed. The optimization strategy of task offloading with wireless and computing resource management (TOWCRM) in mobile edge computing is considered. A resource allocation algorithm based on an improved graph coloring method is used to allocate wireless resource blocks (RBs). The optimal solution of computing resource is obtained by using KKT conditions. To improve the system utility, a semi-distributed TOWCRM strategy is proposed to obtain the task offloading decision. Theoretical simulations under different system parameters are executed, and the proposed semi-distributed TOWCRM strategy can be completed with finite iterations. Simulation results have verified the effectiveness of the proposed algorithm.


IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Chit Wutyee Zaw ◽  
Shashi Raj Pandey ◽  
Kitae Kim ◽  
Choong Seon Hong

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