Reliable Diversity-Based Spatial Crowdsourcing by Moving Workers

TL;DR

This paper addresses reliable diversity-based spatial crowdsourcing by assigning moving workers to time-sensitive tasks, maximizing reliability and diversity, and proposing efficient algorithms and indexing methods.

Contribution

It introduces the RDB-SC problem, proves its NP-hardness, and offers three approximation algorithms along with a dynamic index for efficient solutions.

Findings

Algorithms outperform baseline methods in efficiency and accuracy.

The index significantly reduces retrieval costs.

Proposed approaches are effective on real and synthetic datasets.

Abstract

With the rapid development of mobile devices and the crowdsourcig platforms, the spatial crowdsourcing has attracted much attention from the database community, specifically, spatial crowdsourcing refers to sending a location-based request to workers according to their positions. In this paper, we consider an important spatial crowdsourcing problem, namely reliable diversity-based spatial crowdsourcing (RDB-SC), in which spatial tasks (such as taking videos/photos of a landmark or firework shows, and checking whether or not parking spaces are available) are time-constrained, and workers are moving towards some directions. Our RDB-SC problem is to assign workers to spatial tasks such that the completion reliability and the spatial/temporal diversities of spatial tasks are maximized. We prove that the RDB-SC problem is NP-hard and intractable. Thus, we propose three effective approximation approaches, including greedy, sampling, and divide-and-conquer algorithms. In order to improve the efficiency, we also design an effective cost-model-based index, which can dynamically maintain moving workers and spatial tasks with low cost, and efficiently facilitate the retrieval of RDB-SC answers. Through extensive experiments, we demonstrate the efficiency and effectiveness of our proposed approaches over both real and synthetic data sets.