Mobile crowd sensing systems have been widely used in various domains but are currently facing new challenges. On one hand, the increasingly complex services need a large number of participants to satisfy their demand for sensory data with multidimensional high quality-of-information (QoI) requirements. On the other hand, the willingness of their participation is not always at a high level due to the energy consumption and its impacts on their regular activities. In this paper, we introduce a new metric, called “QoI satisfaction ratio,” to quantify how much collected sensory data can satisfy a multidimensional task's QoI requirements in terms of data granularity and quantity. Furthermore, we propose a participant sampling behavior model to quantify the relationship between the initial energy and the participation of participants. Finally, we present a QoI-aware energy-efficient participant selection approach to provide a suboptimal solution to the defined optimization problem. Finally, we have compared our proposed scheme with existing methods via extensive simulations based on the real movement traces of ordinary citizens in Beijing. Extensive simulation results well justify the effectiveness and robustness of our approach.