Mobile crowdsensing harnesses the sensing power of modern smartphones to collect and analyze data beyond the scale of what was previously possible with traditional sensor networks. Given the participatory nature of mobile crowdsensing, it is imperative to incentivize mobile users to provide sensing services in a timely and reliable manner. Most importantly, given sensed information is often valid for a limited period of time, the capability of smartphone users to execute sensing tasks largely depends on their mobility pattern, which is often uncertain. For this reason, in this paper, we propose IncentMe, a framework that solves this core issue by leveraging game-theoretical reverse auction mechanism design. After demonstrating that the proposed problem is NP-hard, we derive two mechanisms that are parallelizable and achieve higher approximation ratio than existing work. IncentMe has been extensively evaluated on a road traffic monitoring application implemented using mobility traces of taxi cabs in San Francisco, Rome, and Beijing. Results demonstrate that the mechanisms in IncentMe outperform the state of the art work by improving the efficiency in recruiting participants by 30 percent.
|Number of pages||14|
|Journal||IEEE Transactions on Mobile Computing|
|State||Published - Jul 1 2019|
Bibliographical noteFunding Information:
The authors sincerely thank the anonymous reviewers for their comments, which have helped them to significantly improve the quality of this manuscript. This material is based upon work supported by the National Science Foundation under grant no. CCF-1725755, CCF-1533918, CNS-1545037, and CNS-1545050. The information reported in this manuscript does not necessarily reflect the position or the policy of the Federal Government.
© 2002-2012 IEEE.
- game theory
ASJC Scopus subject areas
- Computer Networks and Communications
- Electrical and Electronic Engineering