DPIM: Dynamic Pricing Incentive Mechanism for Mobile Crowd Sensing

As an emerging paradigm for collecting sensory data, Mobile Crowd Sensing (MCS) technology has found widespread application. The successful application of MCS technology relies not only on the active participation of participants but also on the continuous demand for sensing task from data requestors. However, existing researchers predominantly focus on designing participant incentive mechanisms to attract participant to engage in the sensing activities, while the incentive mechanisms for data requestors are rarely addressed. To address the gap, we conceptualize the interactions between data requestors and participants as a queueing process. Building upon utility theory, we propose Dynamic Pricing Incentive Mechanism (DPIM) that dynamically offers optimal incentive guidance to the sensing platform. Moreover, we devise two distinct utility optimization modes for data requestors: one for maximizing their utility and the other for achieving utility equilibrium. These modes are tailored to meet the distinct utility requirement of the sensing platform and data requestors. Through simulations and theoretical analysis, we demonstrate that DPIM effectively provides incentives for the sensing platform across different utility modes.