| Summary: | 碩士 === 國立臺灣大學 === 電機工程學研究所 === 105 === Crowdsensing is an approach to collect human activities and surrounding environment which takes the benefit of the pervasive smartphones and their powerful sensors. In a crowdsensing system, a large number of users in the sensing tasks collect and send data through their mobile devices to a data collection server. The performance of the system heavily depends on the crowd participation. Thus, incentive mechanisms are important in crowdsensing.
We focus on the server-centric model, in which the server has more control over the payment. We design four incentive mechanisms using Stackelberg game, where the server is the leader while the users are the followers. We study the original scenario in which the total reward is fixed and a different scenario in which the total reward is proportional to the effort spent or information collected. The reward is distributed to the users in proportion to either the amount of time spent or quantity of information. We assume that the ability of information collect is the same for all users, but the value of resources may be different. Based on the above reward models and distribution methods, we formulate four different models called TR-T, TR-Q, DR-T, DR-Q models respectively. We study the cases with homogeneous and heterogeneous users. For homogeneous users, we can prove the existence and uniqueness of pure strategy Stackelberg equilibrium in TR-T, TR-Q, DR-Q models. For heterogeneous users, there is a unique Stackelberg equilibrium in TR-T and DR-Q models. We compute the efficiency which measured by PoA and PoS for homogeneous models and DR-Q model with heterogeneous users. The PoA is bounded by a constant except for some special cases in TR-T and TR-Q models.
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