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dc.contributor.authorMachado, R.
dc.contributor.authorHe, H.
dc.contributor.authorWang, G.
dc.contributor.authorTekinay, Şirin
dc.descriptionDue to copyright restrictions, the access to the full text of this article is only available via subscription.
dc.description.abstractWhile dense random deployment satisfies coverage and sensing requirements, constructing dense networks of sensor nodes poses the problems of obtaining node location information.We provide an analytical framework for estimating the redundancy in a single-hop WSN of random deployment of nodes without the need of location information of nodes. We use an information theoretic approach to estimate the redundancy and provide the Cramer-Rao bound on the error in the estimation. We illustrate this redundancy estimation approach and calculate the bounds on the error in the estimation for a WSN with 1-redundancy. We also analytically show the inter-dependence between redundancy and network lifetime for random deployment. We further study the energy model of a WSN as interdependence between the environmental variation and its impact on the energy consumption at individual nodes. Defining network energy as the sum of residual battery energy at nodes, we provide an analytical framework for the dependence of node energy and sensitivity of network energy as a function of environmental variation and reliability parameters. Using a neural network based approach, we perform adaptive density control and show how reliability requirements and environment variation influences the rate of change of network energy.
dc.publisherOld City Publishing
dc.relation.ispartofAd-Hoc and Sensor Wireless Networks
dc.titleRedundancy estimation and adaptive density control inwireless sensor networksen_US
dc.contributor.departmentÖzyeğin University
dc.contributor.authorID(ORCID & YÖK ID 118968) Tekinay, Şirin
dc.contributor.ozuauthorTekinay, Şirin
dc.subject.keywordsWireless sensor networks
dc.subject.keywordsRedundancy estimation
dc.subject.keywordsEnvironment variation
dc.subject.keywordsNeural networks

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