PSC：一个基于模式的时空众包平台，以提高性能，可靠性和隐私
PSC: A Pattern-Based Temporal and Spatial Crowdsourcing Platform to Improve Performance, Reliability, and Privacy
摘要：
在本文中，我们研究了一种新的空间众包系统中的工作人员的时间可用性和他们的空间位置是已知的先验。因此，不仅基于人类工作者的当前位置和区域中可用的任务，而且还基于在给定任务应当被接受、处理和完成的特定时间期间工作者的可用性，来执行对工作者的任务分配。让系统确定工人的日常模式（通过工人注册时的预定义调查问卷，或通过从工人的移动设备存档数据，或通过在路上和实时输入的状态数据）消除了许多不成功的任务分配，因此显著提高了系统的效率。在最初的空间众包（SC）框架中，SC服务器在每个时间实例以及每当新任务或新工作者进入系统时都在本地优化任务分配。我们的新框架（PSC），另一方面，允许用户进入他们的日常工作，时间，空间和可用性模式先验。这使得系统更加稳定和模式机会主义。PSC服务器可以专注于接收和归档新条目（例如，工作者、任务以及它们的标准）在繁忙时间（例如，当系统中有许多新条目时），并且可以集中在安静时间期间的优化和计算（例如，当系统中的新条目较少时）。让任务优化过程发生在安静的时候，当系统几乎没有变化时，使性能更加稳定和可靠。它还允许PSC系统具有系统的全局视图，并执行全局优化以提高性能。
英文摘要：
In this paper, we study a novel spatial crowdsourcing system where the workers' time availabilities and their spatial locations are known a priori. Consequently, the tasks assignment to workers is performed not only based on the current location of the human workers and the tasks available in the region, but also based on the availability of the workers during the specific times that a given task should be accepted, processed, and completed. Having the system determine the daily pattern of the workers (either by predefined questionnaires when the workers register, or by archiving data from the worker's mobile devices, or by on the road and real-time entered status data) eliminates many unsuccessful task assignments and therefore significantly increases the efficiency of the system. In the original Spatial Crowdsourcing (SC) framework, the SC-server optimizes the task assignment locally at every instance of time and whenever a new task, or a new worker, enters the system. Our new framework (PSC), on the other hand, allows the users to enter their daily routine, and temporal, spatial, and availability patterns a priori. This makes the system much more stable and pattern-opportunistic. The PSC servers can focus on receiving and archiving new entries (e.g., workers, tasks, and their criteria) during busy times (e.g., when there are many new entries in the system), and can focus on optimization and computations during quiet times (e.g., when there are fewer new entries in the system). Having the task optimization process happen during quiet times, and when there are few changes to the system, makes the performance more stable and reliable. It also allows the PSC system to have a global view of the system and and perform global optimizations to improve the performance.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
