Yang Jian, Ban Xiaojuan, Xing Chunxiao
School of Computer and Communication Engineering, Beijing Key Laboratory of Knowledge Engineering for Materials Science, University of Science and Technology Beijing, Beijing 100083, China.
Research Institute of Information, Beijing National Research Center for Information Science and Technology, Department of Computer Science and Technology, Institute of Internet Industry, Tsinghua University, Beijing 100084, China.
Sensors (Basel). 2019 Jul 18;19(14):3158. doi: 10.3390/s19143158.
With the rapid development of mobile networks and smart terminals, mobile crowdsourcing has aroused the interest of relevant scholars and industries. In this paper, we propose a new solution to the problem of user selection in mobile crowdsourcing system. The existing user selection schemes mainly include: (1) find a subset of users to maximize crowdsourcing quality under a given budget constraint; (2) find a subset of users to minimize cost while meeting minimum crowdsourcing quality requirement. However, these solutions have deficiencies in selecting users to maximize the quality of service of the task and minimize costs. Inspired by the marginalism principle in economics, we wish to select a new user only when the marginal gain of the newly joined user is higher than the cost of payment and the marginal cost associated with integration. We modeled the scheme as a marginalism problem of mobile crowdsourcing user selection (MCUS-marginalism). We rigorously prove the MCUS-marginalism problem to be NP-hard, and propose a greedy random adaptive procedure with annealing randomness (GRASP-AR) to achieve maximize the gain and minimize the cost of the task. The effectiveness and efficiency of our proposed approaches are clearly verified by a large scale of experimental evaluations on both real-world and synthetic data sets.
随着移动网络和智能终端的快速发展，移动众包引起了相关学者和行业的关注。在本文中，我们针对移动众包系统中的用户选择问题提出了一种新的解决方案。现有的用户选择方案主要包括：（1）在给定预算约束下找到用户子集以最大化众包质量；（2）找到用户子集以在满足最低众包质量要求的同时最小化成本。然而，这些解决方案在选择用户以最大化任务服务质量和最小化成本方面存在不足。受经济学中的边际主义原则启发，我们希望仅在新加入用户的边际收益高于支付成本和与整合相关的边际成本时才选择新用户。我们将该方案建模为移动众包用户选择的边际主义问题（MCUS - 边际主义）。我们严格证明了MCUS - 边际主义问题是NP难问题，并提出了一种具有退火随机性的贪婪随机自适应过程（GRASP - AR）以实现任务收益最大化和成本最小化。通过对真实世界和合成数据集进行的大规模实验评估，清楚地验证了我们提出的方法的有效性和效率。
