Mobile crowd sensing is a new paradigm that enables smart mobile devices to collect and share various types of sensing data in urban environments. However, new challenges arise: one is how to evaluate the quality of data each user potentially capable of providing; another is how to allocate satisfactory yet profitable amount of reward to keep them participating in future tasks. In this paper, we first introduce a mathematical model for characterizing quality of sensing data to be contributed by mobile users. Then, we present a utility function and formulate an optimization problem to maximize the amount of high quality data, given a limited task budget. We next present an effective quality-aware incentive mechanism to solve this problem for online scenarios where participants may arrive or leave at any time. Moreover, the proposal allows the platform to provide selected participants extra bonus according to task completion level and their previous performance to motivate them further. We formally showed the proposed mechanism has the desirable properties of truthfulness, individual rationality, budgetary feasibility and computational efficiency. We compare the proposed scheme with existing methods using a real dataset. Extensive simulation results well justify the effectiveness and robustness of the proposed approach, e.g, compared with another online method "OMG", the gap to the optimum for our proposed Online-QIM approach is reduce by 33.3% when budget <formula><tex>$B = 1000$</tex></formula>.
- data quality
- incentive mechanism design
- Mobile crowd sensing
ASJC Scopus subject areas
- Computer Networks and Communications
- Electrical and Electronic Engineering