Matching-Based Hybrid Service Trading for Task Assignment Over Dynamic Mobile Crowdsensing Networks

Houyi Qi, Minghui Liwang, Seyyedali Hosseinalipour, Xiaoyu Xia, Zhipeng Cheng, Xianbin Wang, Zhenzhen Jiao

Abstract: 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, we introduce a series of stable matching mechanisms, which are integrated into a novel hybrid service trading paradigm consisting of futures trading and spot trading modes, to ensure seamless MCS service provisioning. In futures trading, 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 spot trading, 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 confirm the satisfaction of these properties in practical network settings and demonstrate our commendable performance in terms of service quality, running time, and decision-making overheads, e.g., delay and energy consumption.