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Multi-Worker-Aware Task Planning in Real-Time Spatial Crowdsourcing

  • Qian Tao
  • Yuxiang Zeng
  • Zimu Zhou
  • Yongxin Tong
  • Lei Chen
  • Ke Xu
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10828)

Abstract

Spatial crowdsourcing emerges as a new computing paradigm with the development of mobile Internet and the ubiquity of mobile devices. The core of many real-world spatial crowdsourcing applications is to assign suitable tasks to proper workers in real time. Many works only assign a set of tasks to each worker without making the plan how to perform the assigned tasks. Others either make task plans only for a single worker or are unable to operate in real time. In this paper, we propose a new problem called the Multi-Worker-Aware Task Planning (MWATP) problem in the online scenario, in which we not only assign tasks to workers but also make plans for them, such that the total utility (revenue) is maximized. We prove that the offline version of MWATP problem is NP-hard, and no online algorithm has a constant competitive ratio on the MWATP problem. Two heuristic algorithms, called Delay-Planning and Fast-Planning, are proposed to solve the problem. Extensive experiments on synthetic and real datasets verify the effectiveness and efficiency of the two proposed algorithms.

Keywords

Spatial crowdsourcing Task assignment Task planning 

Notes

Acknowledgment

Qian Tao, Yongxin Tong and Ke Xu’s works are partially supported by the National Science Foundation of China (NSFC) under Grant No. 61502021 and 71531001, National Grand Fundamental Research 973 Program of China under Grant 2014CB340300, the Base construction and Training Programme Foundation for the Talents of Beijing under Grant No. Z171100003217092, and the Science and Technology Major Project of Beijing under Grant No. Z171100005117001. Yuxiang Zeng and Lei Chen’s works are partially supported by the Hong Kong RGC GRF Project 16207617, the National Science Foundation of China (NSFC) under Grant No. 61729201, Science and Technology Planning Project of Guangdong Province, China, No. 2015B010110006, Webank Collaboration Research Project, and Microsoft Research Asia Collaborative Research Grant.

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Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Qian Tao
    • 1
  • Yuxiang Zeng
    • 2
  • Zimu Zhou
    • 3
  • Yongxin Tong
    • 1
  • Lei Chen
    • 2
  • Ke Xu
    • 1
  1. 1.SKLSDE Lab and BDBCBeihang UniversityBeijingChina
  2. 2.The Hong Kong University of Science and TechnologyHong Kong SARChina
  3. 3.Laboratory TIKETH ZurichZurichSwitzerland

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