Convex Optimization
Proceedings of the 11th annual international conference on Mobile computing and networking
eBay in the Sky: strategy-proof wireless spectrum auctions
Proceedings of the 14th ACM international conference on Mobile computing and networking
Nericell: rich monitoring of road and traffic conditions using mobile smartphones
Proceedings of the 6th ACM conference on Embedded network sensor systems
Proceedings of the 7th international conference on Mobile systems, applications, and services
VTrack: accurate, energy-aware road traffic delay estimation using mobile phones
Proceedings of the 7th ACM Conference on Embedded Networked Sensor Systems
Ear-phone: an end-to-end participatory urban noise mapping system
Proceedings of the 9th ACM/IEEE International Conference on Information Processing in Sensor Networks
MAUI: making smartphones last longer with code offload
Proceedings of the 8th international conference on Mobile systems, applications, and services
PRISM: platform for remote sensing using smartphones
Proceedings of the 8th international conference on Mobile systems, applications, and services
A survey of mobile phone sensing
IEEE Communications Magazine
FOCS '10 Proceedings of the 2010 IEEE 51st Annual Symposium on Foundations of Computer Science
Short paper: PEPSI---privacy-enhanced participatory sensing infrastructure
Proceedings of the fourth ACM conference on Wireless network security
MAP: Multiauctioneer Progressive Auction for Dynamic Spectrum Access
IEEE Transactions on Mobile Computing
For sale : your data: by : you
Proceedings of the 10th ACM Workshop on Hot Topics in Networks
Maximizing Non-monotone Submodular Functions
SIAM Journal on Computing
On the approximability of budget feasible mechanisms
Proceedings of the twenty-second annual ACM-SIAM symposium on Discrete Algorithms
Recruitment framework for participatory sensing data collections
Pervasive'10 Proceedings of the 8th international conference on Pervasive Computing
Fair and resilient incentive tree mechanisms
Proceedings of the 2013 ACM symposium on Principles of distributed computing
Proceedings of the third ACM international symposium on Design and analysis of intelligent vehicular networks and applications
iMac: strategy-proof incentive mechanism for mobile crowdsourcing
WASA'13 Proceedings of the 8th international conference on Wireless Algorithms, Systems, and Applications
Social welfare maximization in participatory smartphone sensing
WASA'13 Proceedings of the 8th international conference on Wireless Algorithms, Systems, and Applications
No "one-size fits all": towards a principled approach for incentives in mobile crowdsourcing
Proceedings of the 15th Workshop on Mobile Computing Systems and Applications
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Mobile phone sensing is a new paradigm which takes advantage of the pervasive smartphones to collect and analyze data beyond the scale of what was previously possible. In a mobile phone sensing system, the platform recruits smartphone users to provide sensing service. Existing mobile phone sensing applications and systems lack good incentive mechanisms that can attract more user participation. To address this issue, we design incentive mechanisms for mobile phone sensing. We consider two system models: the platform-centric model where the platform provides a reward shared by participating users, and the user-centric model where users have more control over the payment they will receive. For the platform-centric model, we design an incentive mechanism using a Stackelberg game, where the platform is the leader while the users are the followers. We show how to compute the unique Stackelberg Equilibrium, at which the utility of the platform is maximized, and none of the users can improve its utility by unilaterally deviating from its current strategy. For the user-centric model, we design an auction-based incentive mechanism, which is computationally efficient, individually rational, profitable, and truthful. Through extensive simulations, we evaluate the performance and validate the theoretical properties of our incentive mechanisms.