Conference Proceeding Article
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.
Bayesian game, Mechanism design, all-pay auction, crowd-sensing, network economics, perturbation analysis
Software and Cyber-Physical Systems
IEEE Infocom 2014: IEEE Conference on Computer Communications, April 27-May 2, 2014, Toronto, ON, Canada
City or Country
LUO, Tie; TAN, Hwee-Pink; and XIA, Lirong.
Profit-Maximizing Incentive for Participatory Sensing. (2014). IEEE Infocom 2014: IEEE Conference on Computer Communications, April 27-May 2, 2014, Toronto, ON, Canada. 127-135. Research Collection School Of Information Systems.
Available at: http://ink.library.smu.edu.sg/sis_research/2937
Copyright Owner and License
Creative Commons License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.