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Block-Sparsity-based Localization in Wireless Sensor Networks

Alessandro Bay, Diego Carrera, Sophie M. Fosson, Pasqualina Fragneto, Marco Grella, Chiara Ravazzi, Enrico Magli

EURASIP Journal on Wireless Communications and Networking, vol. 2015 n. 182, pp. 1-15, 2015


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.

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