Multistrategy Repeated Game‐Based Mobile Crowdsourcing Incentive Mechanism for Mobile Edge Computing in Internet of Things

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.

Overlapping Coalition Formation Game via Multi-Objective Optimization for Crowdsensing Task Allocation

“…However, this scheme is difficult to apply in multi-task scenarios. Study [24] proposes a multi-strategy repeated game task allocation mechanism that provides high-quality services to users and ensures the authenticity of user data without manipulating their bids. Another game-theoretic approach introduced in [25] is the worker decision game based on congestion game theory, considering the competitive behavior among users.…”

Towards Crowdsourcing Internet of Things (Crowd-IoT): Architectures, Security and Applications

“…Chi [122] applied game theory to illustrate a long-term incentive strategy for motivating participating workers to supply continuous and long-term rich data for MCS systems. The proposed method viewed cooperation among workers and requester as a recurring game and then generates a long-term incentive using past data and a discount factor.…”