Implementing a Distance Estimator for a Wildlife Tracking System Based on 802.15.4

In this work, a novel distance estimation mechanism using received signal strength indication (RSSI) signals with ZigBee modules is designed, implemented and tested in several scenarios. This estimator was used for a research project focused on a wildlife behavioral classification system deployed in...

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Detalles Bibliográficos
Autores: Cascado Caballero, Daniel, Durán López, Lourdes, Domínguez Morales, Juan Pedro, Gutiérrez Galán, Daniel, Amaya Rodríguez, Claudio Antonio, Domínguez Morales, Manuel Jesús
Tipo de recurso: artículo
Estado:Versión publicada
Fecha de publicación:2019
País:España
Institución:Universidad de Sevilla (US)
Repositorio:idUS. Depósito de Investigación de la Universidad de Sevilla
OAI Identifier:oai:idus.us.es:11441/97781
Acceso en línea:https://hdl.handle.net/11441/97781
https://doi.org/10.3390/electronics8121438
Access Level:acceso abierto
Palabra clave:Distance estimation
802.15.4
ZigBee
Wildlife
Animal tracking
RSSI
Signal filtering
Descripción
Sumario:In this work, a novel distance estimation mechanism using received signal strength indication (RSSI) signals with ZigBee modules is designed, implemented and tested in several scenarios. This estimator was used for a research project focused on a wildlife behavioral classification system deployed in Doñana’s National Park. As a supporting feature for that project, this work was implemented for locating animal’s collars acting as wireless nodes in order to find those who went outside of the coverage area of the network or that were accidentally detached from animals. This work describes the system architecture and the implementation of a mobile assistant capable of recovering devices located beyond the coverage of the network. The analytical model needed for distance estimation and the signal filtering are described, as well as the difficulties that the researchers must deal when building robust location estimators. This theoretical model was applied to three different scenarios and tested with two validation experiments.