Publication Type

Conference Proceeding Article

Version

acceptedVersion

Publication Date

6-2018

Abstract

In spatial crowdsourcing, requesters submit their task-related locations and increase the demand of a local area. The platform prices these tasks and assigns spatial workers to serve if the prices are accepted by requesters. There exist mature pricing strategies which specialize in tackling the imbalance between supply and demand in a local market. However, in global optimization, the platform should consider the mobility of workers; that is, any single worker can be the potential supply for several areas, while it can only be the true supply of one area when assigned by the platform. The hardness lies in the uncertainty of the true supply of each area, hence the existing pricing strategies do not work. In the paper, we formally define this Global Dynamic Pricing(GDP) problem in spatial crowdsourcing. And since the objective is concerned with how the platform matches the supply to areas, we let the matching algorithm guide us how to price. We propose a MAtching-based Pricing Strategy (MAPS) with guaranteed bound. Extensive experiments conducted on the synthetic and real datasets demonstrate the effectiveness of MAPS.

Keywords

Spatial Crowdsourcing, Pricing Strategy

Discipline

Software Engineering

Research Areas

Software and Cyber-Physical Systems

Publication

Proceedings of the 2018 International Conference on Management of Data, Houston, TX, USA, June 10-15

First Page

773

Last Page

788

ISBN

9781450317436

Identifier

10.1145/3183713.3196929

Publisher

ACM

City or Country

Houston, TX, USA

Additional URL

https://doi.org/10.1145/3183713.3196929

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