GeoInformatica

, Volume 20, Issue 3, pp 529–568 | Cite as

Task selection in spatial crowdsourcing from worker’s perspective

  • Dingxiong Deng
  • Cyrus Shahabi
  • Ugur Demiryurek
  • Linhong Zhu
Article
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Abstract

With the progress of mobile devices and wireless broadband, a new eMarket platform, termed spatial crowdsourcing is emerging, which enables workers (aka crowd) to perform a set of spatial tasks (i.e., tasks related to a geographical location and time) posted by a requester. In this paper, we study a version of the spatial crowdsourcing problem in which the workers autonomously select their tasks, called the worker selected tasks (WST) mode. Towards this end, given a worker, and a set of tasks each of which is associated with a location and an expiration time, we aim to find a schedule for the worker that maximizes the number of performed tasks. We first prove that this problem is NP-hard. Subsequently, for small number of tasks, we propose two exact algorithms based on dynamic programming and branch-and-bound strategies. Since the exact algorithms cannot scale for large number of tasks and/or limited amount of resources on mobile platforms, we propose different approximation algorithms. Finally, to strike a compromise between efficiency and accuracy, we present a progressive algorithms. We conducted a thorough experimental evaluation with both real-world and synthetic data on desktop and mobile platforms to compare the performance and accuracy of our proposed approaches.

Keywords

Crowdsourcing Spatial crowdsourcing Spatial task assignment 
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Notes

Acknowledgments

This research has been funded in part by NSF grants IIS-1115153 and IIS-1320149, a contract with Los Angeles Metropolitan Transportation Authority (LA Metro), the USC Integrated Media Systems Center (IMSC), HP Labs and unrestricted cash gifts from Google, Northrop Grumman, Microsoft and Oracle. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of any of the sponsors such as the National Science Foundation or LA Metro.

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Copyright information

© Springer Science+Business Media New York 2016

Authors and Affiliations

  • Dingxiong Deng
    • 1
  • Cyrus Shahabi
    • 1
  • Ugur Demiryurek
    • 1
  • Linhong Zhu
    • 2
  1. 1.Computer Science DepartmentUniversity of Southern CaliforniaLos AngelesUSA
  2. 2.Information Sciences InstituteUniversity of Southern CaliforniaMarina Del ReyUSA

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