Name
Abstract | ||
|---|---|---|
Mobile Crowdsensing (MCS) is a powerful sensing paradigm, which provides sufficient social data for cognitive analytics in industrial sensing, and industrial manufacturing. Considering the sensing costs, sparse MCS, as a variant, only senses the data in a few subareas, and then infers the data of unsensed subareas by the spatio-temporal relationship of the sensed data. Existing works usually assum... |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1109/TII.2020.3028616 | IEEE Transactions on Industrial Informatics |
Keywords | DocType | Volume |
Sensors,Sparse matrices,Correlation,Manufacturing,Task analysis,Inference algorithms,Data models | Journal | 17 |
Issue | ISSN | Citations |
9 | 1551-3203 | 2 |
PageRank | References | Authors |
0.40 | 0 | 8 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| En Wang | 1 | 21 | 8.13 |
| Mijia Zhang | 2 | 2 | 0.40 |
| Xiaochun Cheng | 3 | 75 | 10.15 |
| Yongjian Yang | 4 | 39 | 14.05 |
| Wenbin Liu | 5 | 31 | 11.66 |
| Huaizhi Yu | 6 | 2 | 0.40 |
| Liang Wang | 7 | 4 | 3.12 |
| Jian Zhang | 8 | 2 | 0.40 |