With the proliferation of GPS-enabled smart devices and increased availability of wireless network, spatial crowdsourcing (SC) has been recently proposed as a framework to automatically request workers (i.e., smart device carriers) to perform location-sensitive tasks (e.g., taking scenic photos, reporting events). In this paper, we study a destination-aware task assignment problem that concerns the optimal strategy of assigning each task to proper worker such that the total number of completed tasks can be maximized whilst all workers can reach their destinations before deadlines after performing assigned tasks. Finding the global optimal assignment turns out to be an intractable problem since it does not imply optimal assignment for individual worker. Observing that the task assignment dependency only exists amongst subsets of workers, we utilize tree-decomposition technique to separate workers into independent clusters and develop an efficient depth-first search algorithm with progressive bounds to prune non-promising assignments. In order to make our proposed framework applicable to more scenarios, we further optimize the original framework by proposing strategies to reduce the overall travel cost and allow each task to be assigned to multiple workers. Extensive empirical studies verify that the proposed technique and optimization strategies perform effectively and settle the problem nicely.

中文翻译：

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空间众包中的目的地感知任务分配：一种工人分解方法

随着支持 GPS 的智能设备的普及和无线网络可用性的提高，最近有人提出空间众包 (SC) 作为自动请求工作人员（即智能设备运营商）执行位置敏感任务（例如，拍摄风景）的框架。照片、报道事件）。在本文中，我们研究了一个目的地感知任务分配问题，该问题涉及将每个任务分配给合适的工人的最佳策略，这样可以最大化完成任务的总数，同时所有工人在执行分配的任务后都可以在截止日期前到达目的地。找到全局最优分配原来是一个棘手的问题，因为它并不意味着单个工人的最优分配。观察到任务分配依赖性仅存在于工人的子集之间，我们利用树分解技术将工人分成独立的集群，并开发了一种有效的深度优先搜索算法，该算法具有渐进边界来修剪无希望的分配。为了使我们提出的框架适用于更多场景，我们通过提出降低整体旅行成本并允许将每个任务分配给多个工人的策略来进一步优化原始框架。大量的实证研究验证了所提出的技术和优化策略有效执行并很好地解决了问题。我们通过提出降低总体差旅成本并允许将每个任务分配给多个工人的策略来进一步优化原始框架。大量的实证研究验证了所提出的技术和优化策略有效执行并很好地解决了问题。我们通过提出降低总体差旅成本并允许将每个任务分配给多个工人的策略来进一步优化原始框架。大量的实证研究验证了所提出的技术和优化策略有效执行并很好地解决了问题。