Participant Selection For Crowdsourcing
Disaster Information
Author(s)
E. T.-H. Chu, C.-Y. Lin, P. H. Tsai & J. W. S. Liu
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.
Keywords: crowdsourcing, social network, disaster management.
Keywords
crowdsourcing, social network, disaster management
Related Book
Other papers in this volume
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