Spatial crowdsourcing (SC) is a popular distributed problem-solving paradigm that harnesses the power of mobile workers (e.g., smartphone users) to perform location-based tasks (e.g., checking product placement or taking landmark photos). Typically, a worker needs to travel physically to the target location to finish the assigned task. Hence, the worker’s familiarity level on the target location directly influences the completion quality of the task. In addition, from the perspective of the SC server, it is desirable to finish all tasks with a low recruitment cost. Combining these issues, we propose a Bi-Objective Task Planning (BOTP) problem in SC, where the server makes a task assignment and schedule for the workers to jointly optimize the workers’ familiarity levels on the locations of assigned tasks and the total cost of worker recruitment. The BOTP problem is proved to be NP-hard and thus intractable. To solve this challenging problem, we propose two algorithms: a divide-and-conquer algorithm based on the constraint method and a heuristic algorithm based on the multi-objective simulated annealing algorithm. The extensive evaluations on a real-world dataset demonstrate the effectiveness of the proposed algorithms.

中文翻译：

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空间众包中考虑位置熟悉度的任务规划

空间众包 (SC) 是一种流行的分布式问题解决范例，它利用移动工作人员（例如，智能手机用户）的力量来执行基于位置的任务（例如，检查产品放置或拍摄具有里程碑意义的照片）。通常，工作人员需要亲自前往目标位置才能完成分配的任务。因此，工人对目标位置的熟悉程度直接影响任务的完成质量。此外，从 SC 服务器的角度来看，希望以较低的招聘成本完成所有任务。结合这些问题，我们提出了 SC 中的双目标任务规划 (BOTP) 问题，其中服务器为工人进行任务分配和调度，以共同优化工人对分配任务位置的熟悉程度和总成本工人招聘。BOTP 问题被证明是 NP 难的，因此难以处理。为了解决这个具有挑战性的问题，我们提出了两种算法：基于约束方法的分治算法和基于多目标模拟退火算法的启发式算法。对真实世界数据集的广泛评估证明了所提出算法的有效性。