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Budget-Feasible Online Incentive Mechanisms for Crowdsourcing Tasks Truthfully

机译:预算切实可行的在线众包任务激励机制

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摘要

Mobile crowd sensing (MCS) is a new paradigm that takes advantage of pervasive mobile devices to efficiently collect data, enabling numerous novel applications. To achieve good service quality for an MCS application, incentive mechanisms are necessary to attract more user participation. Most existing mechanisms apply only for the offline scenario where all users report their strategic types in advance. On the contrary, we focus on a more realistic scenario where users arrive one by one online in a random order. Based on the online auction model, we investigate the problem that users submit their private types to the crowdsourcer when arriving, and the crowdsourcer aims at selecting a subset of users before a specified deadline for maximizing the value of services (assumed to be a nonnegative monotone submodular function) provided by selected users under a budget constraint. We design two online mechanisms, OMZ and OMG, satisfying the computational efficiency, individual rationality, budget feasibility, truthfulness, consumer sovereignty, and constant competitiveness under the zero arrival-departure interval case and a more general case, respectively. Through extensive simulations, we evaluate the performance and validate the theoretical properties of our online mechanisms.
机译:移动人群感知(MCS)是一种新的范例,它利用无处不在的移动设备来有效地收集数据,从而实现了许多新颖的应用程序。为了使MCS应用程序获得良好的服务质量,必须采用激励机制来吸引更多的用户参与。大多数现有机制仅适用于所有用户提前报告其战略类型的离线情况。相反,我们关注的是一种更现实的情况,即用户以随机顺序一个人一个人到达在线。基于在线拍卖模型,我们调查了用户到达时将其私人类型提交给众包商的问题,众包商旨在在指定的期限之前选择一部分用户,以最大化服务的价值(假定为非负单调)次模函数)由选定用户在预算约束下提供。我们设计了两种在线机制OMZ和OMG,分别在零起降间隔情况下和更一般的情况下分别满足计算效率,个人合理性,预算可行性,真实性,消费者主权和恒定竞争力。通过广泛的仿真,我们评估了性能并验证了在线机制的理论特性。

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