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Large-Scale Spatiotemporal Fracture Data Completion in Sparse CrowdSensing | IEEE Journals & Magazine | IEEE Xplore

Large-Scale Spatiotemporal Fracture Data Completion in Sparse CrowdSensing


Abstract:

Mobile CrowdSensing (MCS) is a widely adopted approach that involves engaging mobile users to collaboratively perform diverse sensing tasks. In Sparse CrowdSensing, the c...Show More

Abstract:

Mobile CrowdSensing (MCS) is a widely adopted approach that involves engaging mobile users to collaboratively perform diverse sensing tasks. In Sparse CrowdSensing, the completion of data from partially-sensed sources plays a pivotal role in urban sensing applications. This process is essential as it enables efficient data representation, enhances urban analysis capabilities, and ultimately facilitates informed city planning decisions. By leveraging the power of mobile users, Sparse CrowdSensing contributes to the comprehensive understanding of urban environments, enabling effective utilization of data for optimizing urban infrastructure and fostering sustainable urban development. To achieve accurate completion results, previous methods usually utilize the universal similarity and conventional tendency while incorporating only a single dataset to infer the full map. However, in real-world scenarios, there may exist many kinds of data (inter-data), that could help to complement each other. Moreover, for each kind of data (intra-data), there usually exists a few but important spatiotemporal fracture data which caused by the special events (e.g. data loss, equipment failure, etc.), which may behave in a different way as the statistical patterns. Thus, it is an essential task to consider spatiotemporal fracture data caused by the special cases in spatiotemporal data inference, especially using both intra- and inter-data, because of the following challenges: 1) the sparsity of the sensed data, 2) the complex spatiotemporal relations and 3) the uncertain scale of a spatiotemporal fracture. To this end, focusing on the large-scale spatiotemporal fracture, we propose a data completion method that exploits both intra- and inter-data correlations for enhancing performance. Specifically, for the purpose of generating spatiotemporal fracture data, there is Stacked Generative Matrix Completion (\mathcal {SGMC}) by combining previous Stacked Deep Matrix Factorization (SDMF) an...
Published in: IEEE Transactions on Mobile Computing ( Volume: 23, Issue: 7, July 2024)
Page(s): 7585 - 7601
Date of Publication: 05 December 2023

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