Profit-Maximizing Incentive for Participatory Sensing

Abstract

We design an incentive mechanism based on all-pay auctions for participatory sensing. The organizer (principal) aims to attract a high amount of contribution from participating users (agents) while at the same time lowering his payout, which we formulate as a profit-maximization problem. We use a contribution-dependent prize function in an environment that is specifically tailored to participatory sensing, namely incomplete information (with information asymmetry), risk-averse agents, and stochastic population. We derive the optimal prize function that induces the maximum profit for the principal, while satisfying strict individual rationality (i.e., strictly have incentive to participate at equilibrium) for both risk-neutral and weakly risk-averse agents. The thus induced profit is demonstrated to be higher than the maximum profit induced by constant (yet optimized) prize. We also show that our results are readily extensible to cases of risk-neutral agents and deterministic populations.

Meeting Name

33rd IEEE Conference on Computer Communications, IEEE INFOCOM 2014 (2014: Apr. 27-May 2, Toronto, ON, Canada)

Department(s)

Computer Science

Keywords and Phrases

all-pay auction; Bayesian game; crowdsensing; Mechanism design; network economics; perturbation analysis

International Standard Book Number (ISBN)

978-147993360-0

International Standard Serial Number (ISSN)

0743-166X

Document Type

Article - Conference proceedings

Document Version

Citation

File Type

text

Language(s)

English

Rights

© 2014 Institute of Electrical and Electronics Engineers (IEEE), All rights reserved.

Publication Date

01 May 2014

Link to Full Text

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