Toward optimal participant decisions with voting-based incentive model for crowd sensing

Abstract With the rapid development of crowd sensing in sensing applications, excellent incentive mechanisms are playing an increasingly important role. However, most existing solutions do not fully consider the ability of participants to perform tasks, the degree to which they complete tasks, or the credibility of the task sensing results. In this paper, we aim to develop an incentive model based on voting mechanism for crowd sensing(abbreviated as CIBV), which includes three algorithms. The first is a participant decision algorithm (PDA) that adopts a reverse auction model and comprehensively considers candidate execution capability; the second is the budget balance and extra reward algorithm (BBER); the third is the evaluate algorithm (EA) to be applied at the end of sensing tasks. Compared with previous work, the experimental results show that in our proposed CIBV model, each task is performed by multiple participants, and each participant can perform multiple tasks, our model can greatly improve the participants’ execution ability value and provide the platform with the ability to control the process of selecting participants.

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