基于声誉的移动众包系统的在线激励机制

在大数据环境下，对移动众包系统的研究已经成为移动社会网络（MSN）的研究热点。然而由于网络个体的自私性，容易导致移动众包系统的不可信问题，为了激励个体对可信策略的选取，提出一种基于声誉的移动众包系统的激励机制——RMI。首先，结合演化博弈理论和生物学中的Wright-Fisher模型研究移动众包系统的可信演化趋势；在此基础上，分别针对free-riding问题和false-reporting问题建立相应的声誉更新方法，从而形成一套完整的激励机制，激励感知用户和任务请求者对可信策略的选取；最后通过模拟实验对提出的激励机制的有效性和适应性进行了验证。结果显示，与传统的基于社会规范的声誉更新方法相比，RMI有效地提高了移动众包系统的可信性。

Online incentive mechanism based on reputation for mobile crowdsourcing system

In big data environment, the research on mobile crowdsourcing system has become a research hotspot in Mobile Social Network (MSN). However, the selfishness of individuals in networks may cause the distrust problem of mobile crowdsourcing system. In order to inspire individuals to select trustful strategy, an online incentive mechanism based on reputation for mobile crowdsourcing system named RMI was proposed. Combining evolutionary game theory and Wright-Fisher model in biology, the evolution trend of mobile crowdsourcing system was studied. To solve free-riding and false-reporting problems, the reputation updating methods were established. Based on the above researches, an online incentive mechanism was built, which can inspire workers and requesters to select trustful strategies. The simulation results verify the effectiveness and adaptability of the proposed incentive mechanism. Compared with the traditional social norm-based reputation updating method, RMI can improve the trust degree of mobile crowdsourcing system effectively.