Task Offloading in Wireless Powered Mobile Crowd Sensing: A Matching-Based Approach

Mobile crowd sensing (MCS) is a new sensing paradigm that leverages participatory sensing data from mobile devices for accomplishing large-scale sensing tasks. Incentivizing device owners to contribute high-quality sensing data is a prerequisite for the success of MCS services. In this paper, we first propose a pre-contracting incentive mechanism that involves the participation of not only the device owners located in close proximity to Point of Interests (Pols) but also the device owners that are going to pass through those locations. Furthermore, the quality of sensing data is guaranteed through the use of redundancy. In particular, sensing data from multiple device owners is processed and compared at an edge side (i.e., base station) so as to detect the measurement error at the proximity of data sources. Simulation results confirm that the proposed incentive mechanism is efficient in terms of improving the total utility.