Name
Title | ||
|---|---|---|
Spatial Inference In Sensor Networks Using Multiple Hypothesis Testing And Bayesian Clustering |
Abstract | ||
|---|---|---|
The problem of statistical inference in large-scale sensor networks observing spatially varying fields is addressed. A method based on multiple hypothesis testing and Bayesian clustering is proposed. The method identifies homogeneous regions in a field based on similarity in decision statistics and locations of the sensors. High detection power is achieved while keeping false positives at a tolerable level. A variant of the EM-algorithm is employed to associate sensors with clusters. The performance of the method is studied in simulation using different detection theoretic criteria. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.23919/EUSIPCO.2019.8902986 | 2019 27TH EUROPEAN SIGNAL PROCESSING CONFERENCE (EUSIPCO) |
Keywords | DocType | ISSN |
IoT, p-values, Distributed Inference, Statistical Signal Processing, Large-Scale Sensor Networks, BIC | Conference | 2076-1465 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Martin Golz | 1 | 46 | 10.68 |
| Michael Muma | 2 | 144 | 19.51 |
| Topi Halme | 3 | 0 | 1.69 |
| Abdelhak M. Zoubir | 4 | 1036 | 148.03 |
| Visa Koivunen | 5 | 1917 | 187.81 |