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dc.contributor.authorPinzón Trejos, Cristian
dc.contributor.authorTapia, Dante
dc.contributor.authorDe Paz, Juan
dc.contributor.authorAlonso, Ricardo
dc.contributor.authorPinzón, Cristian
dc.contributor.authorBajo, Javier
dc.contributor.authorCorchado, Juan
dc.date.accessioned2018-06-05T19:02:02Z
dc.date.available2018-06-05T19:02:02Z
dc.date.issued01/01/2013
dc.date.issued01/01/2013
dc.identifier.urihttp://ridda2.utp.ac.pa/handle/123456789/4781
dc.descriptionWireless sensor networks (WSNs) have become much more relevant in recent years, mainly because they can be used in a wide diversity of applications. Real-time locating systems (RTLSs) are one of the most promising applications based on WSNs and represent a currently growing market. Specifically, WSNs are an ideal alternative to develop RTLSs aimed at indoor environments where existing global navigation satellite systems, such as the global positioning system, do not work correctly due to the blockage of the satellite signals. However, accuracy in indoor RTLSs is still a problem requiring novel solutions. One of the main challenges is to deal with the problems that arise from the effects of the propagation of radiofrequency waves, such as attenuation, diffraction, reflection and scattering. These effects can lead to other undesired problems, such as multipath. When the ground is responsible for wave reflections, multipath can be modeled as the ground reflection effect. This paper presents an innovative mathematical model for improving the accuracy of RTLSs, focusing on the mitigation of the ground reflection effect by using multilayer perceptron artificial neural networks.en_US
dc.description.abstractWireless sensor networks (WSNs) have become much more relevant in recent years, mainly because they can be used in a wide diversity of applications. Real-time locating systems (RTLSs) are one of the most promising applications based on WSNs and represent a currently growing market. Specifically, WSNs are an ideal alternative to develop RTLSs aimed at indoor environments where existing global navigation satellite systems, such as the global positioning system, do not work correctly due to the blockage of the satellite signals. However, accuracy in indoor RTLSs is still a problem requiring novel solutions. One of the main challenges is to deal with the problems that arise from the effects of the propagation of radiofrequency waves, such as attenuation, diffraction, reflection and scattering. These effects can lead to other undesired problems, such as multipath. When the ground is responsible for wave reflections, multipath can be modeled as the ground reflection effect. This paper presents an innovative mathematical model for improving the accuracy of RTLSs, focusing on the mitigation of the ground reflection effect by using multilayer perceptron artificial neural networks.en_US
dc.formatapplication/pdf
dc.languageeng
dc.language.isoengen_US
dc.rightshttps://creativecommons.org/licenses/by-nc-sa/4.0/
dc.rightsinfo:eu-repo/semantics/openAccess
dc.subjectWireless sensor networksen_US
dc.subjectReal-time location systemsen_US
dc.subjectArtificial neural networksen_US
dc.subjectGround reflection effecten_US
dc.subjectWireless sensor networks
dc.subjectReal-time location systems
dc.subjectArtificial neural networks
dc.subjectGround reflection effect
dc.titleMitigation of the ground reflection effect in real-time locating systems based on wireless sensor networks by using artificial neural networksen_US
dc.typeinfo:eu-repo/semantics/article
dc.typeinfo:eu-repo/semantics/publishedVersion


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