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.

翻译：通过机会性地吸引移动用户（工作者），移动群智感知网络已成为促进异构移动设备间感知/收集数据共享的重要途径。为实现工作者间的任务分配并确保低开销，本文引入了一系列稳定匹配机制，并将其整合到由期货交易模式和现货交易模式构成的新型混合服务交易范式中，以保障无缝的移动群智感知服务供给。在期货交易模式下，我们通过支持超额预订的提前多对多匹配机制确定每个任务的长期工作者集合，同时基于统计分析刻画相关风险。在现货交易模式下，我们研究了长期工作者资源波动对任务服务质量要求违反的影响，并在实际预算约束下，针对违反服务质量要求的任务形式化构建了现货交易模式，其中任务与工作者之间的映射通过现场多对多匹配和现场多对一匹配实现。理论上我们证明所提出的匹配机制满足稳定性、个体理性、公平性和计算效率。综合评估结果不仅验证了这些性质在实际网络设置下的满足情况，还展示了在运行时间、参与者交互及服务质量方面的优异性能。