Centralized and decentralized detection with cost-constrained measurements

buir.contributor.authorGezici, Sinan
dc.citation.epage18en_US
dc.citation.spage8en_US
dc.citation.volumeNumber132en_US
dc.contributor.authorLaz, E.en_US
dc.contributor.authorGezici, Sinanen_US
dc.date.accessioned2018-04-12T11:10:29Z
dc.date.available2018-04-12T11:10:29Z
dc.date.issued2017en_US
dc.departmentDepartment of Electrical and Electronics Engineeringen_US
dc.description.abstractOptimal detection performance of centralized and decentralized detection systems is investigated in the presence of cost constrained measurements. For the evaluation of detection performance, Bayesian, Neyman–Pearson and J-divergence criteria are considered. The main goal for the Bayesian criterion is to minimize the probability of error (more generally, the Bayes risk) under a constraint on the total cost of the measurement devices. In the Neyman–Pearson framework, the probability of detection is to be maximized under a given cost constraint. In the distance based criterion, the J-divergence between the distributions of the decision statistics under different hypotheses is maximized subject to a total cost constraint. The probability of error expressions are obtained for both centralized and decentralized detection systems, and the optimization problems are proposed for the Bayesian criterion. The probability of detection and probability of false alarm expressions are obtained for the Neyman–Pearson strategy and the optimization problems are presented. In addition, J-divergences for both centralized and decentralized detection systems are calculated and the corresponding optimization problems are formulated. The solutions of these problems indicate how to allocate the cost budget among the measurement devices in order to achieve the optimum performance. Numerical examples are presented to discuss the results.en_US
dc.embargo.release2019-03-01en_US
dc.identifier.doi10.1016/j.sigpro.2016.09.012en_US
dc.identifier.issn0165-1684
dc.identifier.urihttp://hdl.handle.net/11693/37334
dc.language.isoEnglishen_US
dc.publisherElsevier B.V.en_US
dc.relation.isversionofhttp://dx.doi.org/10.1016/j.sigpro.2016.09.012en_US
dc.source.titleSignal Processingen_US
dc.subjectCentralized detectionen_US
dc.subjectDecentralized detectionen_US
dc.subjectHypothesis testingen_US
dc.subjectMeasurement costen_US
dc.subjectSensor networksen_US
dc.titleCentralized and decentralized detection with cost-constrained measurementsen_US
dc.typeArticleen_US
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