Data Quality Guided Incentive Mechanism Design for Crowdsensing - 2018


In crowdsensing, applicable rewards are forever expected to compensate the participants for his or her consumptions of physical resources and involvements of manual efforts. While continuous low quality sensing information might do harm to the supply and preciseness of crowdsensing primarily based services, few existing incentive mechanisms have ever addressed the issue of information quality. The design of quality primarily based incentive mechanism is motivated by its potential to avoid inefficient sensing and unnecessary rewards. During this Project, we incorporate the thought of knowledge quality into the look of incentive mechanism for crowdsensing, and propose to pay the participants as how well they are doing, to motivate the rational participants to efficiently perform crowdsensing tasks. This mechanism estimates the quality of sensing data, and offers every participant a present based on her effective contribution. We have a tendency to conjointly implement the mechanism and evaluate its improvement in terms of quality of service and profit of service supplier. The evaluation results show that our mechanism achieves superior performance when put next to general information assortment model and uniform pricing theme.

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