PROJECT TITLE :
Data Quality Guided Incentive Mechanism Design for Crowdsensing - 2017
ABSTRACT:
In crowdsensing, appropriate rewards are perpetually expected to compensate the participants for his or her consumptions of physical resources and involvements of manual efforts. Whereas continuous low quality sensing knowledge might do harm to the supply and preciseness of crowdsensing based services, few existing incentive mechanisms have ever addressed the problem of knowledge quality. The design of quality primarily based incentive mechanism is motivated by its potential to avoid inefficient sensing and unnecessary rewards. During this paper, we incorporate the thought of knowledge quality into the planning of incentive mechanism for crowdsensing, and propose to pay the participants as how well they do, to motivate the rational participants to efficiently perform crowdsensing tasks. This mechanism estimates the standard of sensing knowledge, and offers every participant a souvenir based on her effective contribution. We additionally implement the mechanism and evaluate its improvement in terms of quality of service and profit of service supplier. The analysis results show that our mechanism achieves superior performance compared to general data assortment model and uniform pricing scheme.
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