Name
Abstract | ||
|---|---|---|
In this paper, we deal with the localization problem in wireless sensor networks, where a target sensor location must be estimated starting from few measurements of the power present in a radio signal received from sensors with known locations. Inspired by the recent advances in sparse approximation, the localization problem is recast as a block-sparse signal recovery problem in the discrete spatial domain. In this paper, we develop different RSS-fingerprinting localization algorithms and propose a dictionary optimization based on the notion of the coherence to improve the reconstruction efficiency. The proposed protocols are then compared with traditional fingerprinting methods both via simulation and on-field experiments. The results prove that our methods outperform the existing ones in terms of the achieved localization accuracy. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1186/s13638-015-0410-6 | EURASIP Journal on Wireless Communications and Networking |
Keywords | Field | DocType |
Block-sparsity, Localization, Real data experimentation/testbed, RSS-fingerprinting | Computer science,Sparse approximation,Computer network,Signal recovery,Real-time computing,Coherence (physics),Wireless sensor network,Radio signal | Journal |
Volume | Issue | ISSN |
2015 | 1 | 1687-1499 |
Citations | PageRank | References |
5 | 0.41 | 11 |
Authors | ||
7 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Alessandro Bay | 1 | 5 | 0.75 |
| Diego Carrera | 2 | 43 | 7.09 |
| Sophie M. Fosson | 3 | 44 | 8.96 |
| Pasqualina Fragneto | 4 | 131 | 14.36 |
| Marco Grella | 5 | 11 | 0.90 |
| Chiara Ravazzi | 6 | 114 | 13.23 |
| Enrico Magli | 7 | 1319 | 114.81 |