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SPACE-TA
2017
ACM Transactions on Intelligent Systems and Technology
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We propose a novel crowdsensing task allocation framework called SPACE-TA (SPArse Cost-Effective Task Allocation), combining compressive sensing, statistical analysis, active learning, and transfer learning ...
In particular, we exploit both intradata correlations within the same type of sensed data and interdata correlations among different types of sensed data in the sensing task. ...
With the aforementioned research objective and challenges, our main contributions are: (1) We propose a novel practical MCS task allocation mechanism, called SPACE-TA (SPArse and Cost-Effective Task Allocation ...
doi:10.1145/3131671
fatcat:nlbkzybf6zhezjfwt73vlkjpdi
Multi-dimensional Urban Sensing in Sparse Mobile CrowdSensing
2019
IEEE Access
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A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2021; you can also visit the original URL.
The file type is application/pdf.
We exploit the intra-and intertask correlations in data inference to deduce the data of the unsensed cells through the multi-task compressive sensing and then learn and select the most effective cell, ...
To effectively capture the intra-and inter-task correlations in cell selection, we design a network structure with multiple branches, where branches extract the intra-task correlations for each task, respectively ...
[11] first considered the intradata and interdata correlations, while they only exploited the correlations to the data inference without jointly considering the cell selection. ...
doi:10.1109/access.2019.2924184
fatcat:btnuao3hfjdvrd7rofr5jheg6i