Non-Aligned Multi-Scale Data Completion for Sparse Mobile CrowdSensing

Sparse Mobile CrowdSensing has emerged as a practical method for data collection, recruiting mobile users to collect partial data and leveraging spatiotemporal correlations to infer the missing data. To improved the QoS of crowdsourced data, existing methods typically assume that the scales of collected data are similar. However, in real-world scenarios, the diversity of user devices results in data collections that vary in scale. More importantly, the collected coarser-scale data points often do not perfectly correspond to multiple finer-scale data points, resulting in highly complex compositional relationships and posing significant challenges for multi-scale data completion. To address these challenges, this paper proposes a multi-scale data completion framework designed to process and integrate multi-scale data with non-aligned compositional relationships. We first align features across scales using the least common multiple scaling, then enhance the interaction and integration of data across scales through a bidirectional processing strategy and modified Mamba architectures, specifically ST-Mamba and Cross-Mamba. Evaluated on six real-world datasets, our study demonstrates the effectiveness of the proposed framework in handling multi-scale data completion challenges, particularly when dealing with non-aligned compositional relationships.