论文标题
合作移动众包的随机团队形成方法
A Stochastic Team Formation Approach for Collaborative Mobile Crowdsourcing
论文作者
论文摘要
移动众包（MCS）是通过公开电话将传统上由员工或承包商执行的传统上执行的感应任务（传统上执行的智能手机用户）的广义行为。随着众包应用程序的越来越复杂，请求者发现，通过组建满足其复杂任务要求的熟练工人团队来利用工人之间的协作力量。这种类型的MC被称为协作MCS（CMC）。以前的CMC方法主要仅关注团队技能最大化的方面。关于社交网络（SNS）的其他团队组成研究仅关注社会关系最大化。在本文中，我们提出了一种混合方法，请求者能够聘请一个团队，该团队不仅具有所需的专业知识，而且还具有社会联系，并且可以协作完成任务。由于CMC中的团队成立被证明是NP-HARD，因此我们开发了一种随机算法，该算法利用工人了解其SN邻居，并要求指定的领导者招募合适的团队。所提出的算法是从最佳停止策略中灵感的，并使用赔率 - 叠加仪来计算其输出。实验结果表明，与基准指数最佳解决方案相比，提出的方法减少了计算时间并产生合理的性能结果。
Mobile Crowdsourcing (MCS) is the generalized act of outsourcing sensing tasks, traditionally performed by employees or contractors, to a large group of smart-phone users by means of an open call. With the increasing complexity of the crowdsourcing applications, requesters find it essential to harness the power of collaboration among the workers by forming teams of skilled workers satisfying their complex tasks' requirements. This type of MCS is called Collaborative MCS (CMCS). Previous CMCS approaches have mainly focused only on the aspect of team skills maximization. Other team formation studies on social networks (SNs) have only focused on social relationship maximization. In this paper, we present a hybrid approach where requesters are able to hire a team that, not only has the required expertise, but also is socially connected and can accomplish tasks collaboratively. Because team formation in CMCS is proven to be NP-hard, we develop a stochastic algorithm that exploit workers knowledge about their SN neighbors and asks a designated leader to recruit a suitable team. The proposed algorithm is inspired from the optimal stopping strategies and uses the odds-algorithm to compute its output. Experimental results show that, compared to the benchmark exponential optimal solution, the proposed approach reduces computation time and produces reasonable performance results.
