Participant selection for crowdsourcing disaster information

E. T.H. Chu, C. Y. Lin, P. H. Tsai, J. W.S. Liu

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Citations (Scopus)

Abstract

Experiences with past major disasters tell us that people with wireless devices and social network services can serve effectively as mobile human sensors. A disaster warning and response system can solicit eye-witness reports from selected participants and use information provided by them to supplement surveillance sensor coverage. This paper describes a natural formulation of the participant selection problem that the system needs to solve in order to select participants from available people given their qualities as human sensors and the costs of deploying them. For this, we developed a greedy algorithm, named PSPG, that first calculates the benefit-to-cost (B2C) factor of each participant. It then dispatches participants to regions according to participants' B2C. We compared PSP-G with the two well-known optimization methods, BARON and BONMIN. The results show that PSP-G delivers a near optimal solution with a low time complexity. In particular, the time PSP-G needs can be merely one tenth of the execution time of the existing optimization methods, which makes PSP-G a practical solution for emergency needs in disaster areas.

Original languageEnglish
Title of host publicationDisaster Management and Human Health Risk III - Reducing Risk, Improving Outcomes
Pages231-240
Number of pages10
DOIs
Publication statusPublished - 2013 Nov 25
Event3rd International Conference on Disaster Management and Human Health: Reducing Risk, Improving Outcomes, DMAN 2013 - A Coruna, Spain
Duration: 2013 Jul 92013 Jul 11

Publication series

NameWIT Transactions on the Built Environment
Volume133
ISSN (Print)1743-3509

Other

Other3rd International Conference on Disaster Management and Human Health: Reducing Risk, Improving Outcomes, DMAN 2013
CountrySpain
CityA Coruna
Period13-07-0913-07-11

All Science Journal Classification (ASJC) codes

  • Architecture
  • Civil and Structural Engineering
  • Building and Construction
  • Automotive Engineering
  • Safety, Risk, Reliability and Quality
  • Arts and Humanities (miscellaneous)
  • Transportation
  • Safety Research
  • Computer Science Applications

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  • Cite this

    Chu, E. T. H., Lin, C. Y., Tsai, P. H., & Liu, J. W. S. (2013). Participant selection for crowdsourcing disaster information. In Disaster Management and Human Health Risk III - Reducing Risk, Improving Outcomes (pp. 231-240). (WIT Transactions on the Built Environment; Vol. 133). https://doi.org/10.2495/DMAN130211