eprintid: 518
rev_number: 6
eprint_status: archive
userid: 5
dir: disk0/00/00/05/18
datestamp: 2011-07-11
lastmod: 2013-07-01 11:30:12
status_changed: 2013-07-01 11:30:12
type: techreport
metadata_visibility: show
item_issues_count: 0
creators_name: Viani, Federico
creators_name: Martinelli, Mauro
creators_name: Ioriatti, Luca
creators_name: Benedetti, Manuel
creators_name: Massa, Andrea
title: Passive Real-Time Localization Through Wireless Sensor Networks
ispublished: pub
subjects: TU
full_text_status: public
note: This version is a pre-print of the final version available at IEEE.
abstract: Localization and tracking play a key role in several applications both civilian and military [1]. The growing needs of remote monitoring private and public areas caused a fast development of wireless and pervasive systems. In such a framework, the availability of low-power devices integrating on-board processing and wireless communication stimulated several studies in efficient collaborative signal processing algorithms for tracking purposes. Most of them are based on the exploitation of data collected by dedicated sensor or they assume the target equipped with a transmitting device [2]. In this work, an innovative approach based on a LBE strategy to localize and track passive objects is presented. The localization problem is addressed only by considering the available received signal strength indicator (RSSI) at the nodes of a wireless sensor network (WSN) deployed in the environment and without any additional on board sensor.
date: 2011-01
date_type: published
institution: University of Trento
department: informaticat
refereed: FALSE
referencetext: [1] C.Y. Chong and S. P. Kumar, “Sensor networks: evolution, opportunities, and challenges,” Proc. IEEE, vol. 91, no. 8, pp. 1247–1256, Aug. 2003. [2] G. Latsoudas and N. D. Sidiropoulos, “A fast and effective multidimensional scaling approach for node localization in wireless sensor networks,” IEEE Trans. Geosci. Remote Sens., vol. 55, no. 10, pp. 5121–5127, Oct. 2007. [3] A. Massa, A. Boni, and M. Donelli, “A classification approach based on SVM for electromagnetic subsurface sensing,” IEEE Trans. Geosci. Remote Sens., vol. 43, no. 9, pp. 2084-2093, Sept. 2005. [4] V. Vapnik, Statistical Learning Theory. New York: Wiley, 1998. [5] J. Platt, “Probabilistic outputs for support vector machines and comparison to regularized likelihood methods,” in “Advances in large margin Classifiers”, (MIT Press, Cambridge, MA, 1999), Eds. A. J. Smola, P. Bartlett, B. Scholkopf, and D. Schuurmans. [6] ArsLogica Corex Datasheet. http://www.arslogica.it/projects/vigilia/vigilia.html.
citation:   Viani, Federico and Martinelli, Mauro and Ioriatti, Luca and Benedetti, Manuel and Massa, Andrea  (2011) Passive Real-Time Localization Through Wireless Sensor Networks.  [Technical Report]     
document_url: http://www.eledia.org/students-reports/518/1/DISI-11-186.C181.pdf