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Although near-to-optimal solutions based on Turbo and LDPC codes exist for this problem, in most cases the proposed techniques do not scale to networks of hundreds of sensors. We present a scalable solution based on the following key elements: (a) distortion-optimized index assignments for low-complexity distributed quantization, (b) source-optimized hierarchical clustering based on the Kullback-Leibler distance and (c) sum-product decoding on specific factor graphs exploiting the correlation of the data.<\/jats:p>","DOI":"10.1145\/1525856.1525862","type":"journal-article","created":{"date-parts":[[2009,6,9]],"date-time":"2009-06-09T12:44:12Z","timestamp":1244551452000},"page":"1-32","update-policy":"http:\/\/dx.doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":11,"title":["Low-complexity coding and source-optimized clustering for large-scale sensor networks"],"prefix":"10.1145","volume":"5","author":[{"given":"Gerhard","family":"Maierbacher","sequence":"first","affiliation":[{"name":"Instituto de Telecomunica\u00e7\u00f5es, Universidade do Porto, Porto, Portugal"}]},{"given":"Jo\u00e3o","family":"Barros","sequence":"additional","affiliation":[{"name":"Instituto de Telecomunica\u00e7\u00f5es, Universidade do Porto, Porto, Portugal"}]}],"member":"320","published-online":{"date-parts":[[2009,6,4]]},"reference":[{"key":"e_1_2_1_1_1","unstructured":"Aho A. 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