A Novel User Selection Strategy with Incentive Mechanism Based on Time Window in Mobile Crowdsensing

With the rapid development of smart phones and wireless communication, mobile sensing has become an efficient environmental data acquisition method capable of accomplishing large-scale and highly complex sensing tasks. Currently, participants want to collect continuous data over a period of time. However, the number of participants varies widely in some periods. In view of this application background, this paper proposes a new incentive mechanism of extra rewards: premium and jackpot incentive mechanism (PJIM), and a new participant selection method based on time window: participant selection for time window dependent tasks (PS-TWDT). In the PJIM, the platform divides the time period of sensing tasks according to the time distribution of task participants and adopts different incentive strategies in different situations; at the same time, it introduces the prize pool mechanism to attract more participants to participate in the sensing task with fewer participants. In the PS-TWDT, we design a participant selection method based on dynamic programming algorithm. The goal is to maximize the data benefit while the sensing time of the selected participants covers the task time period. In addition, the updating strategy of participants’ credit value is added, and the credit value of participants is updated according to their willingness to participate in the task and data quality. Finally, simulation experiment verifies that the incentive mechanism and participant selection method proposed in this paper have good performance.