  By opportunistically engaging mobile users (workers), mobile crowdsensing
(MCS) networks have emerged as important approach to facilitate sharing of
sensed/gathered data of heterogeneous mobile devices. To assign tasks among
workers and ensure low overheads, a series of stable matching mechanisms is
introduced in this paper, which are integrated into a novel hybrid service
trading paradigm consisting of futures trading mode and spot trading mode to
ensure seamless MCS service provisioning. In the futures trading mode, we
determine a set of long-term workers for each task through an
overbooking-enabled in-advance many-to-many matching (OIA3M) mechanism, while
characterizing the associated risks under statistical analysis. In the spot
trading mode, we investigate the impact of fluctuations in long-term workers'
resources on the violation of service quality requirements of tasks, and
formalize a spot trading mode for tasks with violated service quality
requirements under practical budget constraints, where the task-worker mapping
is carried out via onsite many-to-many matching (O3M) and onsite many-to-one
matching (OMOM). We theoretically show that our proposed matching mechanisms
satisfy stability, individual rationality, fairness and computational
efficiency. Comprehensive evaluations also verify the satisfaction of these
properties under practical network settings, while revealing commendable
performance on running time, participators' interactions, and service quality.
