Mobile crowdsensing is a novel sensing paradigm enabled by the proliferation of mobile devices. Since crowdsensing applications are driven by sufficient users, advanced incentive mechanisms have been designed to enhance users' willingness to participate in sensing tasks. However, incentive mechanisms only provide adequate sensing opportunities on the condition that the available user base is large. If existing users are fewer than the required number of participants, incentive mechanisms will lose efficacy. This article proposes a hybrid framework called HySense to compensate for inadequate sensing opportunities solely provided by incentive mechanisms. Within each sensing cycle, HySense combines mobile devices with static sensor nodes to generate uniformly distributed space-time data under the constraint of field coverage. To balance sensing opportunities among different geographic regions, redundant users are efficiently migrated from densely populated areas to sparsely populated areas. HySense utilizes calibration mode for checking whether the participants' behavior patterns are consistent with the sensing task queue. Therefore, any change caused by unforeseen accidents can be dealt with in advance.