Task allocation of spatial crowdsourcing tasks is an important branch of crowdsourcing. Spatial crowdsourcing tasks not only require workers to complete a specific task at a specified time, but also require users to go to the designated location to complete the corresponding tasks. In this paper, Scope spatial crowdsourcing task whose work position is a region rather than a location is a kind of spatial crowdsourcing task. Mobile crowdsourced sensing (MCS) is one of the most important platforms to publish spatial crowdsourcing tasks, based on which MCS workers can use smartphones to complete the collections of related sensing data. When assigning tasks for scoped crowdsourcing tasks, there is a scope overlap between tasks and one or more tasks due to the association of task scope between tasks, which causes a waste of manpower. The focus of this paper is to study the redundancy of the task scope that occurs when using MCS to collect scoping data in the case of fewer workers and more tasks. Optimizing scope spatial crowdsourcing tasks allocation algorithm (OSSA) can eliminate the redundancy of the task area by integrating and decomposing tasks and achieve the improvement of the assignable number of tasks. In the Windows platform, experiments are made to compare the efficiency of the OSSA algorithm with the greedy algorithm and the two-phase-based global online allocation (TGOA) algorithm to further prove the correctness and feasibility of the algorithm for task scope optimization.

中文翻译：

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范围空间众包任务在线优化分配研究

空间众包任务的任务分配是众包的一个重要分支。空间众包任务不仅要求工作人员在指定时间完成特定任务，还要求用户到指定地点完成相应任务。在本文中，工作位置是区域而不是位置的Scope空间众包任务是一种空间众包任务。移动众包传感（MCS）是发布空间众包任务的最重要平台之一，MCS工作人员可以在此基础上使用智能手机完成相关传感数据的收集。在为范围众包任务分配任务时，由于任务之间的任务范围关联，任务与一个或多个任务之间存在范围重叠，造成人力浪费。本文的重点是研究在较少工人和较多任务的情况下，使用 MCS 收集范围数据时出现的任务范围冗余。优化范围空间众包任务分配算法（OSSA）可以通过对任务的整合和分解来消除任务区域的冗余，实现任务可分配数量的提高。在Windows平台上，通过实验比较了OSSA算法与贪心算法和基于两阶段的全局在线分配（TGOA）算法的效率，进一步证明了该算法对任务范围优化的正确性和可行性。优化范围空间众包任务分配算法（OSSA）可以通过对任务的整合和分解来消除任务区域的冗余，实现任务可分配数量的提高。在Windows平台上，通过实验比较了OSSA算法与贪心算法和基于两阶段的全局在线分配（TGOA）算法的效率，进一步证明了该算法对任务范围优化的正确性和可行性。优化范围空间众包任务分配算法（OSSA）可以通过对任务的整合和分解来消除任务区域的冗余，实现任务可分配数量的提高。在Windows平台上，通过实验比较了OSSA算法与贪心算法和基于两阶段的全局在线分配（TGOA）算法的效率，进一步证明了该算法对任务范围优化的正确性和可行性。