Location Familiarity Oriented Task Planning in Spatial Crowdsourcing

Abstract

Spatial crowdsourcing (SC) is a promising framework for requesting workers (e.g., smartphone users) to perform location-based tasks (e.g., taking scenic photos or checking product placement). Specifically, to perform the assigned task, the worker needs to move physically to the target location, hence his familiarity with the target location has an impact on the efficiency and quality of the task completion. On the other hand, the SC server usually intends to finish all tasks with a low cost. Considering these factors, in this paper, we study a Bi-Objective Task Planning (BOTP) problem in SC, where the workers are assigned and scheduled to perform the appropriate tasks, such that the workers' familiarity with the locations of the spatial tasks and the SC server's cost for recruiting workers are jointly optimized. We prove that the BOTP problem is NP-hard and thus intractable. To tackle the BOTP problem, we propose a heuristic algorithm based on the multi-objective simulated annealing algorithm. The extensive experiments demonstrate the effectiveness of our proposed algorithm over a real-world dataset.