Task Assignment on Multi-Skill Oriented Spatial Crowdsourcing

TL;DR

This paper addresses the complex problem of assigning multi-skilled workers to spatial tasks with constraints, proposing heuristic algorithms to optimize skill matching and maximize worker benefits in spatial crowdsourcing.

Contribution

It introduces the multi-skill spatial crowdsourcing problem, proves its NP-hardness, and develops three heuristic algorithms for effective worker-task assignment.

Findings

Heuristic algorithms outperform baseline methods in efficiency.

Proposed methods achieve high skill matching accuracy.

Algorithms are effective on real and synthetic datasets.

Abstract

With the rapid development of mobile devices and crowdsourcing platforms, the spatial crowdsourcing has attracted much attention from the database community. Specifically, the spatial crowdsourcing refers to sending location-based requests to workers, based on their current positions. In this paper, we consider a spatial crowdsourcing scenario, in which each worker has a set of qualified skills, whereas each spatial task (e.g., repairing a house, decorating a room, and performing entertainment shows for a ceremony) is time-constrained, under the budget constraint, and required a set of skills. Under this scenario, we will study an important problem, namely multi-skill spatial crowdsourcing (MS-SC), which finds an optimal worker-and-task assignment strategy, such that skills between workers and tasks match with each other, and workers' benefits are maximized under the budget constraint. We prove that the MS-SC problem is NP-hard and intractable. Therefore, we propose three effective heuristic approaches, including greedy, g-divide-and-conquer and cost-model-based adaptive algorithms to get worker-and-task assignments. Through extensive experiments, we demonstrate the efficiency and effectiveness of our MS-SC processing approaches on both real and synthetic data sets.