An optimal assignment for mobile sensing tasks in spatial crowdsourcing

Abstract

Crowdsourcing is rising as a new framework that enables human workers to solve tasks in the physical world. With spatial crowdsourcing(SC), a major challenge is to assign reliable workers to nearby tasks. The goal of such task assignment process is to maximize the task completion in the face of uncertainty. Two modes of task assignment have been proposed by Kazemi and Shahabi: worker select task(WST) and server assign task(SAT). In our paper, we propose a SAT model on the dynamic task assignment problem in spatial crowdsourcing. We present the minimum-cost maximum-utility assignment (MC-MUA) problem in spatial crowdsourcing and aim to maximize utility as well as minimize the traveling costs of tasks in spatial. We present the optimal algorithm to solve the problem in a polynomial time using synthetic datasets and conduct the simulation experiment. It turns out that the practical performance of the matching algorithm corroborates the theoretical analysis.