Incentive-aware Task Location in Spatial Crowdsourcing

Fei Zhu, Shushu Liu, Junhua Fang, An Liu*

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

Abstract

With the popularity of wireless network and mobile devices, spatial crowdsourcing has gained much attention from both academia and industry. One of the critical components in spatial crowdsourcing is task-worker matching, where workers are assigned to tasks to meet some pre-defined objectives. Previous works generally assume that the locations of tasks are known in advance. However, this does not always hold, since in many real world applications where to put tasks is not specific and needs to be determined on the fly. In this paper, we propose Incentive-aware Task Location (ITL), a novel problem in spatial crowdsourcing. Given a location-unspecific task with a fixed budget, the ITL problem seeks multiple locations to place the task and allocates the given budget to each location, such that the number of workers who are willing to participate the task is maximized. We prove that the ITL problem is NP-hard and propose three heuristic methods to solve it, including even clustering, uneven clustering and greedy location methods. Through extensive experiments on a real dataset, we demonstrate the efficiency and effectiveness of the proposed methods.

Original languageEnglish
Title of host publicationDatabase Systems for Advanced Applications
Subtitle of host publicationProceedings of 26th International Conference (DASFAA 2021), Part I
EditorsChristian S. Jensen, Ee-Peng Lim, De-Nian Yang, Wang-Chien Lee, Vincent S. Tseng, Vana Kalogeraki, Jen-Wei Huang, Chih-Ya Shen
PublisherSpringer Science and Business Media Deutschland GmbH
Pages650-657
Number of pages8
ISBN (Electronic)9783030731946
ISBN (Print)9783030731939
DOIs
Publication statusPublished - Apr 2021
MoE publication typeA4 Article in a conference publication
EventInternational Conference on Database Systems for Advanced Applications - Virtual, Online, Taipei, Taiwan, Republic of China
Duration: 11 Apr 202114 Apr 2021
Conference number: 26
http://dm.iis.sinica.edu.tw/DASFAA2021/

Publication series

NameLecture Notes in Computer Science
PublisherSpringer
Volume12681
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

ConferenceInternational Conference on Database Systems for Advanced Applications
Abbreviated titleDASFAA
CountryTaiwan, Republic of China
CityTaipei
Period11/04/202114/04/2021
Internet address

Keywords

  • Spatial crowdsourcing
  • Task assignment
  • Task location

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