Name
Title | ||
|---|---|---|
On Designing Data Quality-Aware Truth Estimation and Surplus Sharing Method for Mobile Crowdsensing. |
Abstract | ||
|---|---|---|
Mobile crowdsensing has become a novel and promising paradigm in collecting, analyzing, and exploiting massive amounts of data. However, the issue of data quality has not been carefully addressed. Low quality data contributions undermine the effectiveness and prospects of crowdsensing, and thus motivate the need for approaches to guarantee the high quality of the contributed data. In this paper, w... |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1109/JSAC.2017.2676898 | IEEE Journal on Selected Areas in Communications |
Keywords | Field | DocType |
Estimation,Sensors,Unsupervised learning,Smart phones,Monitoring,Mobile communication | Anomaly detection,Data quality,Incentive,Shapley value,Computer science,Crowdsensing,Exploit,Unsupervised learning,Artificial intelligence,Payment,Machine learning | Journal |
Volume | Issue | ISSN |
35 | 4 | 0733-8716 |
Citations | PageRank | References |
24 | 0.71 | 49 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Shuo Yang | 1 | 330 | 28.54 |
| Wu Fan | 2 | 1731 | 192.15 |
| Tang Shaojie | 3 | 2224 | 157.73 |
| Xiaofeng Gao | 4 | 713 | 98.58 |
| Bo Yang | 5 | 361 | 40.37 |
| guihai chen | 6 | 3537 | 317.28 |