Utlu, İhsan2017-03-012017-03-012017-012017-012017-02-23http://hdl.handle.net/11693/32812Cataloged from PDF version of article.Includes bibliographical references (leaves 46-52).We propose a novel algorithm for distributed processing applications constrained by the available communication resources using diffusion strategies that achieves up to a 103 fold reduction in the communication load over the network, while delivering a comparable performance with respect to the state of the art. After the computation of the local estimates, the information is diffused among the processing elements (or nodes) non-uniformly in time by conditioning the information transfer on level-crossings of the diffused parameter, resulting in a greatly reduced communication requirement. We provide the mean and meansquare stability analyses of the proposed algorithm, and illustrate the gain in communication efficiency compared to other reduced-communication distributed estimation schemes.ix, 52 leaves : charts (some color) ; 29 cm.Englishinfo:eu-repo/semantics/openAccessDistributed estimationAdaptive networksEvent-triggered communicationLevel-crossing quantizationThesisB155244