A New Data Completion Perspective on Sparse CrowdSensing: Spatiotemporal Evolutionary Inference Approach

Mobile CrowdSensing (MCS) has emerged as a popular paradigm to engage mobile users in collaborative sensing tasks. However, its performance is hindered by its limited spatiotemporal range and the cost of data collection. An effective strategy is to integrate Sparse MCS with data completion, allowing for unsensed data inference. However, when confronted with situations where sensed data is excessively sparse, data inference results may be unsatisfactory due to several challenges including: 1) uneven data distribution, 2) complex spatiotemporal correlation, and 3) the presence of inference noise. To address these challenges, we propose a model named Spatiotemporal Evolutionary Inference (STEI) that achieves accurate inference of unsensed data in Sparse MCS. Specifically, we complete the unsensed data by uncovering strong local correlations in the data and gradually evolving those correlations to the global situation. In each evolution step, we thoroughly consider the impact of spatiotemporal consistency and difference. To minimize the interference of noise during the evolution process, we design an adaptive coefficient to enhance the dependence on sensed data. Finally, to validate the effectiveness of STEI, we conduct extensive qualitative and quantitative experiments using three popular datasets. The experimental results demonstrate that our approach excels in accurately inferring data, particularly in situations where the distribution of data is notably uneven.