Name
Abstract | ||
|---|---|---|
Crowdsensing is an effective method to map physical spatial fields by exploiting sensors embedded in smartphones. Enclosing humans in the loop increases the amount of data available for the mapping process, with benefits in terms of accuracy and cost. On the other hand, the huge amount of data generated and the irregular spatial distribution of measurements are serious issues to be addressed. In this paper we propose a combined Gaussian process (GP)-State space method for crowd mapping whose complexity and memory requirements for field representation do not depend on the number of data measured. The method is validated through an experimental campaign and simulations. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1016/j.pmcj.2018.06.001 | Pervasive and Mobile Computing |
Keywords | Field | DocType |
Crowdsensing,Gaussian processes,Spatial field estimation,Environmental mapping | Effective method,Computer science,Crowdsensing,Real-time computing,Gaussian process,Distributed computing | Journal |
Volume | ISSN | Citations |
48 | 1574-1192 | 1 |
PageRank | References | Authors |
0.35 | 23 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Davide Dardari | 1 | 1557 | 116.18 |
| Gianni Pasolini | 2 | 137 | 22.42 |
| Flavio Zabini | 3 | 57 | 9.18 |