Publication Type

Journal Article

Version

Postprint

Publication Date

9-2016

Abstract

Many crowdsourcing scenarios are heterogeneous in the sense that, not only the workers' types (e.g., abilities, costs) are different, but the beliefs (probabilistic knowledge) about their respective types are also different. In this paper, we design an incentive mechanism for such scenarios using an asymmetric all-pay contest (or auction) model. Our design objective is an optimal mechanism, i.e., one that maximizes the crowdsourcing revenue minus cost. To achieve this, we furnish the contest with a prize tuple which is an array of reward functions for each potential winner (worker). We prove and characterize the unique equilibrium of this contest, and also solve the optimal prize tuple. In addition, this study discovers a counter-intuitive property, strategy autonomy (SA), which means that heterogeneous workers behave independently of one another as if they were in a homogeneous setting. In game-theoretical terms, it says that an asymmetric auction admits a symmetric equilibrium. Not only theoretically interseting, but SA also has important practical implications on mechanism complexity, energy efficiency, crowdsourcing revenue, and system scalability. By scrutinizing seven mechanisms, our extensive performance evaluation demonstrates the superior performance of our mechanism as well as offers insights into the SA property.

Keywords

Crowdsourcing, mobile crowd sensing, participatory sensing, all-pay auction, asymmetric auction, strategy autonomy

Discipline

Computer Sciences | Software Engineering

Research Areas

Software and Cyber-Physical Systems

Publication

IEEE Transactions on Mobile Computing

Volume

15

Issue

9

First Page

2234

Last Page

2246

ISSN

1536-1233

Identifier

10.1109/TMC.2015.2485978

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

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/TMC.2015.2485978

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