Demand-Driven Sparse Mobile Crowdsensing With Neighborhood-Aware Reconstruction

Sparse mobile crowdsensing (SMCS) is a cost-effective paradigm aimed at recruiting workers to complete sensing tasks and inferring the remaining unobserved data, with broad applications in large-scale, fine-grained monitoring services. In SMCS, spatial coverage of the sensing area or global completion accuracy is typically used as the performance metric. However, in many real-world service scenarios (e.g., temperature, humidity, air quality monitoring), users are generally only interested in data from their specific regions and expect the highest possible data accuracy. In such cases, relying solely on coverage or global completion error fails to adequately assess the quality of the platform’s service. To address this and satisfy users’ sensing demands as much as possible while maintaining low sensing costs, we propose the demand-driven framework with neighborhood-aware data reconstruction (D2-SMCS), which integrates regional population demand calculation, dynamic clustering, and data reconstruction. Unlike existing approaches, we introduce Quality of Service (QoS) as a performance metric based on regional population demand. First, we quantify the interest level of sensing tasks in different regions by considering factors, such as population demand and data fluctuation. Based on this quantification, the dynamic clustering module selects the regions most beneficial for accurate data completion. Finally, to overcome the limitation of traditional matrix completion methods in capturing short-term variations, we propose an innovative neighborhood-aware latent matrix completion (NALMC) approach to infer and complete the unobserved regions. Extensive experiments on real-world datasets demonstrate the effectiveness of our framework.