  In this paper, we study the incentive mechanism design for real-time data
aggregation, which holds a large spectrum of crowdsensing applications. Despite
extensive studies on static incentive mechanisms, none of these are applicable
to real-time data aggregation due to their incapability of maintaining PUs'
long-term participation. We emphasize that, to maintain PUs' long-term
participation, it is of significant importance to protect their privacy as well
as to provide them a desirable cumulative compensation. Thus motivated, in this
paper, we propose LEPA, an efficient incentive mechanism to stimulate long-term
participation in real-time data aggregation. Specifically, we allow PUs to
preserve their privacy by reporting noisy data, the impact of which on the
aggregation accuracy is quantified with proper privacy and accuracy measures.
Then, we provide a framework that jointly optimizes the incentive schemes in
different time slots to ensure desirable cumulative compensation for PUs and
thereby prevent PUs from leaving the system halfway. Considering PUs' strategic
behaviors and combinatorial nature of the sensing tasks, we propose a
computationally efficient on-line auction with close-to-optimal performance in
presence of NP-hardness of winner user selection. We further show that the
proposed on-line auction satisfies desirable properties of truthfulness and
individual rationality. The performance of LEPA is validated by both
theoretical analysis and extensive simulations.
