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TaskMe: a cross-community, quality-enhanced incentive mechanism for mobile crowd sensing

Incentive is crucial to the success of mobile crowd sensing (MCS) applications. Offering monetary rewards is promising, and many incentive schemes are studied based on reverse auction. However, they all assume a static budget for a task, the dynamics of tasks in terms of sensing contexts as well as user knowledge to the target area is not considered. This may result in uncompleted or overpaid tasks. In addition, existing studies mainly motivate user participation, whereas the quality of data is paid little attention. To address these issues, we propose a novel MCS incentive mechanism called TaskMe. An LBSN-powered reference model for dynamic budgeting is proposed, and a combination of reputation and multi-payment-enhanced reverse auction scheme is used to improve data quality. Initial experiments prove the effectiveness of our system.