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Exploiting the RSSI Long-Term Data of a WSN for the RF Channel Modeling in EPS Environments.
Antayhua, Roddy A R; Pereira, Maicon D; Fernandes, Nestor C; Rangel de Sousa, Fernando.
Afiliação
  • Antayhua RAR; Radio Frequency Laboratory at the Department of Electrical and Electronics Engineering, Federal University of Santa Catarina, Florianopolis, SC 88040-900, Brazil.
  • Pereira MD; Department of Electrical Engineering, Federal Institute of Santa Catarina, Itajai, SC 88007-303, Brazil.
  • Fernandes NC; Radio Frequency Laboratory at the Department of Electrical and Electronics Engineering, Federal University of Santa Catarina, Florianopolis, SC 88040-900, Brazil.
  • Rangel de Sousa F; Department of Electrical and Computer Engineering, Federal University of Bahia, Salvador, BA 40210-630, Brazil.
Sensors (Basel) ; 20(11)2020 May 29.
Article em En | MEDLINE | ID: mdl-32485923
In this paper, we propose a methodology to use the received signal strength indicator (RSSI) available by the protocol stack of an installed Wireless Sensor Network (WSN) at an electric-power-system environment (EPS) as a tool for obtaining the characteristic of its communication channel. Thereby, it is possible to optimize the settings and configuration of the network after its deployment, which is usually run empirically without any previous knowledge of the channel. A study case of a hydroelectric power plant is presented, where measurements recorded over a two-month period were analyzed and treated to obtain the large-scale characteristics of the radiofrequency channel at 2.4 GHz. In addition, we showed that instantaneous RSSI data can also be used to detect specific issues in the network, such as repetitive patterns in the transmitted power level of the nodes, and information about its environment, such as the presence of external sources of electromagnetic interference. As a result, we demonstrate the practical use of the RSSI long-term data generated by the WSN for its own performance optimization and the detection of particular events in an EPS or any similar industrial environment.
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Tipo de estudo: Guideline / Prognostic_studies Idioma: En Revista: Sensors (Basel) Ano de publicação: 2020 Tipo de documento: Article País de afiliação: Brasil País de publicação: Suíça
Texto completo
Adicionar na Minha BVS
Imprimir
XML
PubMed Links
Buscar no Google

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Tipo de estudo: Guideline / Prognostic_studies Idioma: En Revista: Sensors (Basel) Ano de publicação: 2020 Tipo de documento: Article País de afiliação: Brasil País de publicação: Suíça