NDLI: Mobile crowdsensing incentives under participation uncertainty

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Mobile crowdsensing incentives under participation uncertainty

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Mobile crowdsensing incentives under participation uncertainty

Content Provider	ACM Digital Library
Author	Karaliopoulos, Merkourios Koutsopoulos, Iordanis Micholia, Panagiota
Abstract	Mobile crowdsensing applications rely on crowds of contributors who are willing to carry out certain tasks of interest. This willingness varies widely across users and tasks and (monetary) incentives are often engaged to strengthen it or make up for its total absence. These incentives need to carefully account for the highly non-homogeneous response of users to external motivation. We propose a framework that explicitly accounts for the implicit uncertainty about the eventual participation and contributions of users. In the framework, the impact of incentives on the user choice to contribute or not is modeled probabilistically. First, we formulate a convex optimization problem for incentive allocation with the goal of achieving maximum expected quality while taking into account task budget limitations and constraints related to the physical locations of users and tasks. We then propose an iterative algorithm to alleviate the complexity of the original problem with two basic steps: an allocation step that applies incentive allocation to a set of available contributors; and a refinement step that revokes portions of the allocated incentives. We study the performance characteristics of our algorithm by comparing it to a default solver for convex optimization problems.
Starting Page	29
Ending Page	34
Page Count	6
File Format	PDF
ISBN	9781450343435
DOI	10.1145/2940353.2940357
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2016-07-05
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Mobile crowdsensing Incentive allocation Participation uncertainty
Content Type	Text
Resource Type	Article

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