000064306 001__ 64306 000064306 005__ 20190709135526.0 000064306 0247_ $$2doi$$a10.1109/TSP.2017.2740199 000064306 0248_ $$2sideral$$a101882 000064306 037__ $$aART-2017-101882 000064306 041__ $$aeng 000064306 100__ $$0(orcid)0000-0001-5175-3166$$aFernandez-Bes, Jesus 000064306 245__ $$aAdaptive diffusion schemes for heterogeneous networks 000064306 260__ $$c2017 000064306 5060_ $$aAccess copy available to the general public$$fUnrestricted 000064306 5203_ $$aIn this paper, we deal with distributed estimation problems in diffusion networks with heterogeneous nodes, i.e., nodes that either implement different adaptive rules or differ in some other aspect such as the filter structure or length, or step size. Although such heterogeneous networks have been considered from the first works on diffusion networks, obtaining practical and robust schemes to adaptively adjust the combiners in different scenarios is still an open problem. In this paper, we study a diffusion strategy specially designed and suited to heterogeneous networks. Our approach is based on two key ingredients: 1) the adaptation and combination phases are completely decoupled, so that network nodes keep purely local estimations at all times and 2) combiners are adapted to minimize estimates of the network mean-square-error. Our scheme is compared with the standard adapt-Then-combine scheme and theoretically analyzed using energy conservation arguments. Several experiments involving networks with heterogeneous nodes show that the proposed decoupled adapt-Then-combine approach with adaptive combiners outperforms other state-of-The-Art techniques, becoming a competitive approach in these scenarios. 000064306 536__ $$9info:eu-repo/grantAgreement/ES/MINECO/TIN2013-41998-R$$9info:eu-repo/grantAgreement/ES/MINECO/TEC2014-52289-R$$9info:eu-repo/grantAgreement/ES/MINECO/DPI2016-75458-R$$9info:eu-repo/grantAgreement/ES/ISCIII/CIBER-BBN-MULTITOOLS2HEART$$9This project has received funding from the European Union’s Horizon 2020 research and innovation program under grant agreement No H2020 638284-MODELAGE$$9info:eu-repo/grantAgreement/EC/H2020/638284/EU/Is your heart aging well? A systems biology approach to characterize cardiac aging from the cell to the body surface/MODELAGE$$9info:eu-repo/grantAgreement/ES/DGA/T96 000064306 540__ $$9info:eu-repo/semantics/openAccess$$aAll rights reserved$$uhttp://www.europeana.eu/rights/rr-f/ 000064306 590__ $$a4.203$$b2017 000064306 591__ $$aENGINEERING, ELECTRICAL & ELECTRONIC$$b32 / 260 = 0.123$$c2017$$dQ1$$eT1 000064306 592__ $$a1.247$$b2017 000064306 593__ $$aSignal Processing$$c2017$$dQ1 000064306 593__ $$aElectrical and Electronic Engineering$$c2017$$dQ1 000064306 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/acceptedVersion 000064306 700__ $$aArenas-García, Jerónimo 000064306 700__ $$aSilva, Magno T.M. 000064306 700__ $$aAzpicueta-Ruiz, Luis A. 000064306 773__ $$g65, 21 (2017), 5661-5674$$pIEEE trans. signal process.$$tIEEE TRANSACTIONS ON SIGNAL PROCESSING$$x1053-587X 000064306 8564_ $$s2307834$$uhttps://zaguan.unizar.es/record/64306/files/texto_completo.pdf$$yPostprint 000064306 8564_ $$s142849$$uhttps://zaguan.unizar.es/record/64306/files/texto_completo.jpg?subformat=icon$$xicon$$yPostprint 000064306 909CO $$ooai:zaguan.unizar.es:64306$$particulos$$pdriver 000064306 951__ $$a2019-07-09-11:59:42 000064306 980__ $$aARTICLE