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Complicated-Skills-Based Task Assignment in Spatial Crowdsourcing

  • Jiaxu LiuEmail author
  • Haogang Zhu
  • Xiao Chen
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9998)

Abstract

Spatial crowdsourcing is an activity consisting in outsourcing spatial tasks to a community of online, yet on-ground and mobile, workers. Presently an increasing number of spatial crowdsourcing applications emerges due to the related technologies tends to maturity. Distinct from traditional crowdsourcing dualistic entities, task and worker, a special kind of applications imports the third one of skill. Consequently, a novel assignment problem called multiple skills assignment problem (MSAP) is generated which extends the entity relationship from 2 to 3 dimensions. Inspired by group strategy we first propose a lightweight algorithm GMA that could achieve approximate optimal solution quickly. However, GMA exists a defect of ignoring that workers with multiple skills can decrease total travel distance significantly. Thus we propose a revised algorithm RGMA to cut down distance cost. With synthetic datasets, we empirically and comparatively evaluate the performance of the baseline and two proposed algorithms.

Keywords

Assignment Problem Travel Distance Task Assignment Skill Requirement Approximate Optimal Solution 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  1. 1.State Key Laboratory of Software Development EnvironmentBeihang UniversityBeijingChina
  2. 2.School of Computer Science and TechnologyBeijing University of Posts and elecommunicationsBeijingChina

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