Efficient and Load Balancing Strategy for Task Scheduling in Spatial Crowdsourcing

  • Dezhi SunEmail author
  • Yong Gao
  • Dan Yu
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9998)


With the wide use of mobile devices, spatial crowdsourcing platforms are becoming popular. An important problem of spatial crowdsourcing is assigning a set of spatial tasks tagged with location and time for workers according to their location. In most cases, existing approaches usually take the matching algorithm as a fundamental step to solve this problem which aims to maximize the number of completed tasks. However, in the present of many spatial crowdsourcing platforms, how to assign the tasks at high efficiency and make a relatively fair schedule for multiple workers is a new challenge. In this paper, we study the problem of load balancing based task scheduling for multiple workers. We present fast and effective approximate algorithms for task scheduling problem. With both real and synthetic datasets, we verify the effectiveness of our proposed methods.


Task Assignment Task Schedule Spatial Task Maximum Cost Location Entropy 
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 UniversityBeijingPeople’s Republic of China
  2. 2.Key Laboratory of Yunnan Province Universities of the Diversity and Ecological Adaptive Evolution for Animals and Plants on YunGui PlateauQujing Normal UniversityQujingPeople’s Republic of China
  3. 3.China Standard Software Co., Ltd.BeijingPeople’s Republic of China

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