TaskMe: Toward a dynamic and quality-enhanced incentive mechanism for mobile crowd sensing

Incentive is crucial to the success of mobile crowd sensing (MCS) systems. Over the different manners of incentives, providing monetary rewards has been proved quite useful. However, existing monetary-based incentive studies (e.g., the reverse auction based methods) mainly encourage user participation, whereas sensing quality is often neglected. First, the budget setting is static and may not meet the sensing contexts or user anticipation. Second, they do not measure the quality of data contributed. Third, the design of most incentive schemes is quantity- or cost-focused and not quality-oriented. To address these issues, we propose a novel MCS incentive mechanism called TaskMe. An LBSN (location-based social network)-powered model is leveraged for dynamic budgeting and proper worker selection, and a combination of multi-facet quality measurements and a multi-payment-enhanced reverse auction scheme are used to improve sensing quality. Experiments on several user studies and the crawled dataset validate TaskMe's effectiveness.