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.
|Titolo:||Block-sparsity-based localization in wireless sensor networks|
|Data di pubblicazione:||2015|
|Digital Object Identifier (DOI):||10.1186/s13638-015-0410-6|
|Appare nelle tipologie:||1.1 Articolo in rivista|