scholarly journals Multistrategy Repeated Game-Based Mobile Crowdsourcing Incentive Mechanism for Mobile Edge Computing in Internet of Things

2021 ◽  
Vol 2021 ◽  
pp. 1-18
Author(s):  
Chuanxiu Chi ◽  
Yingjie Wang ◽  
Yingshu Li ◽  
Xiangrong Tong

With the advent of the Internet of Things (IoT) era, various application requirements have put forward higher requirements for data transmission bandwidth and real-time data processing. Mobile edge computing (MEC) can greatly alleviate the pressure on network bandwidth and improve the response speed by effectively using the device resources of mobile edge. Research on mobile crowdsourcing in edge computing has become a hot spot. Hence, we studied resource utilization issues between edge mobile devices, namely, crowdsourcing scenarios in mobile edge computing. We aimed to design an incentive mechanism to ensure the long-term participation of users and high quality of tasks. This paper designs a long-term incentive mechanism based on game theory. The long-term incentive mechanism is to encourage participants to provide long-term and continuous quality data for mobile crowdsourcing systems. The multistrategy repeated game-based incentive mechanism (MSRG incentive mechanism) is proposed to guide participants to provide long-term participation and high-quality data. The proposed mechanism regards the interaction between the worker and the requester as a repeated game and obtains a long-term incentive based on the historical information and discount factor. In addition, the evolutionary game theory and the Wright-Fisher model in biology are used to analyze the evolution of participants’ strategies. The optimal discount factor is found within the range of discount factors based on repeated games. Finally, simulation experiments verify the existing crowdsourcing dilemma and the effectiveness of the incentive mechanism. The results show that the proposed MSRG incentive mechanism has a long-term incentive effect for participants in mobile crowdsourcing systems.

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Xinhui Ding ◽  
Wenjuan Zhang

Due to the limited computing resources of the mobile edge computing (MEC) server, a massive Internet of things device computing unloading strategy using game theory in mobile edge computing is proposed. First of all, in order to make full use of the massive local Internet of things equipment resources, a new MEC system computing an unloading system model based on device-to-device (D2D) communication is designed and modeled, including communication model, task model, and computing model. Then, by using the utility function, the parameters are substituted into it, and the optimization problem with the goal of maximizing the number of CPU cycles and minimizing the energy consumption is constructed with the unloading strategy and power as constraints. Finally, the game theory is used to solve the problem of computing offload. Based on the proposed beneficial task offload theory, combined with the mobile user device computing offload task amount, transmission rate, idle device performance, and other factors, the computing offload scheme suitable for their own situation is selected. The simulation results show that the proposed scheme has better convergence characteristics, and, compared with other schemes, the proposed scheme significantly improves the amount of data transmission and reduces the energy consumption of the task.


IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 101539-101549 ◽  
Author(s):  
Hao Wu ◽  
Hui Tian ◽  
Gaofeng Nie ◽  
Pengtao Zhao

2017 ◽  
Vol 35 (11) ◽  
pp. 2606-2615 ◽  
Author(s):  
Xinchen Lyu ◽  
Wei Ni ◽  
Hui Tian ◽  
Ren Ping Liu ◽  
Xin Wang ◽  
...  

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