H2I: A Handover-State Encoding-Based Data Inheritance Method for Mobile Crowdsensing

In mobile crowdsensing (MCS), participants often drop out, requiring their unfinished tasks to be migrated to othersa process known as task migration. However, existing work rarely addresses how to properly handle the residual data from exiting participants, leading to low data utilization and degraded quality, which harms MCS Quality of Service (QoS). To tackle this, we propose H2I, a data inheritance method based on handover-state encoding. H2I first introduces a hierarchical data quality assessment framework: a point-level layer uses taskembedded multi-head attention to estimate collection quality for each record, while a grid-level layer employs an Attention U-Net to assess credibility using spatial context. These scores jointly guide data correction. We find that quality assessment alone cannot distinguish natural variations caused by task migration from true anomalies, often causing over-smoothing. To solve this, H2I incorporates handover-state encoding, which models the spatiotemporal proximity and historical observation states of the exiting participant and the successor. A gated fusion mechanism then uses this encoding to control the direction and scale of correction. Extensive simulations show that H2I outperforms existing methods in MAE, $\mathbf{R}^{\mathbf{2}}$, and other metrics, significantly improving data quality during task migration.