Profit maximization in mobile crowdsourcing: A truthful auction mechanism

In mobile crowdsourcing systems, smartphones can collectively monitor the surrounding environment and share data with the platform of the system. The platform manages the system and encourages smartphone users to contribute to the crowdsourcing system. To enable such sensing system, incentive mechanisms are necessary to motivate users to share the sensing capabilities of their smartphones. In this paper, we propose ProMoT, which is a Profit Maximizing Truthful auction mechanism for mobile crowdsourcing systems. In the proposed auction mechanism, the platform acts as an auctioneer. The smartphone users act as the sellers and submit their bids to the platform. The platform selects a subset of smartphone users and assigns the tasks to them. ProMoT aims to maximize the profit of the platform while providing satisfying rewards to the smartphone users. ProMoT consists of a winner determination algorithm, which is an approximate but close-to-optimal algorithm based on a greedy mechanism, and a payment scheme, which determines the payment to users. Both are computationally efficient with polynomial time complexity. We prove that ProMoT motivates smartphone users to rationally participate and truthfully reveals their bids. Simulation results show that ProMoT increases the profit of the platform in comparison with an existing scheme.

[1]  Huadong Ma,et al.  A behavior-based incentive mechanism for crowd sensing with budget constraints , 2014, 2014 IEEE International Conference on Communications (ICC).

[2]  Fan Ye,et al.  Mobile crowdsensing: current state and future challenges , 2011, IEEE Communications Magazine.

[3]  Vijay V. Vazirani,et al.  Approximation Algorithms , 2001, Springer Berlin Heidelberg.

[4]  Tim Roughgarden,et al.  Algorithmic Game Theory , 2007 .

[5]  Huadong Ma,et al.  Opportunities in mobile crowd sensing , 2014, IEEE Communications Magazine.

[6]  Eric Horvitz,et al.  Combining human and machine intelligence in large-scale crowdsourcing , 2012, AAMAS.

[7]  Wen Hu,et al.  Ear-phone: an end-to-end participatory urban noise mapping system , 2010, IPSN '10.

[8]  Stephen P. Boyd,et al.  Convex Optimization , 2004, Algorithms and Theory of Computation Handbook.

[9]  Athanasios V. Vasilakos,et al.  TRAC: Truthful auction for location-aware collaborative sensing in mobile crowdsourcing , 2014, IEEE INFOCOM 2014 - IEEE Conference on Computer Communications.

[10]  Xi Fang,et al.  Crowdsourcing to smartphones: incentive mechanism design for mobile phone sensing , 2012, Mobicom '12.

[11]  Emiliano Miluzzo,et al.  A survey of mobile phone sensing , 2010, IEEE Communications Magazine.

[12]  Xiang-Yang Li,et al.  How to crowdsource tasks truthfully without sacrificing utility: Online incentive mechanisms with budget constraint , 2014, IEEE INFOCOM 2014 - IEEE Conference on Computer Communications.

[13]  Iordanis Koutsopoulos,et al.  Optimal incentive-driven design of participatory sensing systems , 2013, 2013 Proceedings IEEE INFOCOM.

[14]  E. Maasland,et al.  Auction Theory , 2021, Springer Texts in Business and Economics.

[15]  Nicholas R. Jennings,et al.  Efficient budget allocation with accuracy guarantees for crowdsourcing classification tasks , 2013, AAMAS.