Publication Type

Conference Proceeding Article

Publication Date

4-2014

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.

Keywords

Bayesian game, Mechanism design, all-pay auction, crowd-sensing, network economics, perturbation analysis

Discipline

Software Engineering

Research Areas

Software and Cyber-Physical Systems

Publication

IEEE Infocom 2014: IEEE Conference on Computer Communications, April 27-May 2, 2014, Toronto, ON, Canada

First Page

127

Last Page

135

ISBN

9781479933600

Identifier

10.1109/INFOCOM.2014.6847932

Publisher

IEEE

City or Country

Piscataway, NJ

Copyright Owner and License

Authors

Creative Commons License

Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.

Additional URL

http://doi.org/10.1109/INFOCOM.2014.6847932

Share

COinS