Truthful multicast routing in selfish wireless networks
Proceedings of the 10th annual international conference on Mobile computing and networking
Algorithmic Game Theory
ACE: exploiting correlation for energy-efficient and continuous context sensing
Proceedings of the 10th international conference on Mobile systems, applications, and services
Medusa: a programming framework for crowd-sensing applications
Proceedings of the 10th international conference on Mobile systems, applications, and services
Crowdsourcing to smartphones: incentive mechanism design for mobile phone sensing
Proceedings of the 18th annual international conference on Mobile computing and networking
Zee: zero-effort crowdsourcing for indoor localization
Proceedings of the 18th annual international conference on Mobile computing and networking
Automatically characterizing places with opportunistic crowdsensing using smartphones
Proceedings of the 2012 ACM Conference on Ubiquitous Computing
Context-aware mobile crowdsourcing
Proceedings of the 2012 ACM Conference on Ubiquitous Computing
Using On-the-Move Mining for Mobile Crowdsensing
MDM '12 Proceedings of the 2012 IEEE 13th International Conference on Mobile Data Management (mdm 2012)
Lowering the barriers to large-scale mobile crowdsensing
Proceedings of the 14th Workshop on Mobile Computing Systems and Applications
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Mobile crowdsourcing with smartphones advocates the cooperative effort of mobile smartphones to perform a joint distributed sensing task, which has gained growing importance for its potential to support a wide spectrum of large-scale sensing applications. Smartphone users in the real world are strategic and rational. Thus, one crucial problem in mobile crowdsourcing with smartphones is to stimulate cooperation from smartphone users. Several major challenges should be addressed. First, the actual cost incurred for a sensing task is private information and unknown to other users and the mobile crowdsourcing platform. Second, smartphone users are strategic, which suggest a user may deliberately misreport its cost (different from the real cost) in order to maximize its own utility. In this paper, we propose a strategy-proof incentive mechanism called iMac based on the Vickrey-Clarke-Groves (VCG) mechanism. The main idea of iMac is to stimulate smartphone users to truthfully disclose their real costs in spite of strategic behavior of the users. iMac introduces two main components. The first component determines the allocation of a sensing task to smartphone users given the user costs. And the second component decides the payment to each user. We prove that iMac can successfully produce a unique Nash equilibrium at which each user truthfully discloses the cost. Meanwhile, the minimization of the social cost is achieved. Simulation results demonstrate iMac achieves the desired design objectives and the overpayment is modest.