Abstract
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.
Highlights
The increasing data demand in the 5G era is a huge challenge for Internet of Things (IoT) devices with limited computing power and resources
In order to solve the problem, this paper models the interaction between workers and requesters as repeated games and calculates the specific discount factor value that could maintain the equilibrium
To avoid malicious competition and select highquality crowd workers to improve the utility of the crowdsourcing system, Hu et al [36] proposed an incentive mechanism based on the combination of the reverse auction and multiattribute auction in mobile crowdsourcing
Summary
The increasing data demand in the 5G era is a huge challenge for IoT devices with limited computing power and resources. The incentive mechanism is added in the crowdsourcing process, and a reasonable pricing plan is formulated based on the contribution of workers to complete the task. This paper researches the long-term incentive mechanism to motivate workers to continuously improve their efforts and ensure that workers continuously provide high-quality data. Using the discount factor of the repeated game and historical data, the current behavior of the two sides of the game is proposed, and an effective algorithm is proposed to obtain the Nash equilibrium and find the optimal payoff (4) Both theoretical analysis and simulation experiments show that the proposed incentive mechanism could more effectively motivate workers to continue to provide high-quality data to improve the performance of the platform.
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