Specialty-Aware Task Assignment in Spatial Crowdsourcing

TL;DR

This paper addresses the challenge of assigning specialty-aware tasks in spatial crowdsourcing by considering individual skill-based charges for workers, proposing heuristics to maximize task completion under budget constraints.

Contribution

It introduces a novel specialty-aware task assignment model with skill-specific charges, and develops two heuristics to efficiently solve the NP-hard problem.

Findings

Heuristics outperform baseline methods in effectiveness.

Proposed methods are efficient on synthetic and real datasets.

Skill-specific charging improves fairness and practicality.

Abstract

With the rapid development of Mobile Internet, spatial crowdsourcing is gaining more and more attention from both academia and industry. In spatial crowdsourcing, spatial tasks are sent to workers based on their locations. A wide kind of tasks in spatial crowdsourcing are specialty-aware, which are complex and need to be completed by workers with different skills collaboratively. Existing studies on specialty-aware spatial crowdsourcing assume that each worker has a united charge when performing different tasks, no matter how many skills of her/him are used to complete the task, which is not fair and practical. In this paper, we study the problem of specialty-aware task assignment in spatial crowdsourcing, where each worker has fine-grained charge for each of their skills, and the goal is to maximize the total number of completed tasks based on tasks' budget and requirements on particular skills. The problem is proven to be NP-hard. Thus, we propose two efficient heuristics to solve the problem. Experiments on both synthetic and real datasets demonstrate the effectiveness and efficiency of our solutions.