TY - GEN
T1 - Profit-maximizing incentive for participatory sensing
AU - Luo, Tie
AU - Tan, Hwee Pink
AU - Xia, Lirong
PY - 2014
Y1 - 2014
N2 - 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.
AB - 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.
KW - all-pay auction
KW - Bayesian game
KW - crowdsensing
KW - Mechanism design
KW - network economics
KW - perturbation analysis
UR - http://www.scopus.com/inward/record.url?scp=84904429783&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84904429783&partnerID=8YFLogxK
U2 - 10.1109/INFOCOM.2014.6847932
DO - 10.1109/INFOCOM.2014.6847932
M3 - Conference contribution
AN - SCOPUS:84904429783
SN - 9781479933600
T3 - Proceedings - IEEE INFOCOM
SP - 127
EP - 135
BT - IEEE INFOCOM 2014 - IEEE Conference on Computer Communications
T2 - 33rd IEEE Conference on Computer Communications, IEEE INFOCOM 2014
Y2 - 27 April 2014 through 2 May 2014
ER -