Cargando…

A Low-Cost, Low-Power, Multisensory Device and Multivariable Time Series Prediction for Beehive Health Monitoring

We present a custom platform that integrates data from several sensors measuring synchronously different variables of the beehive and wirelessly transmits all measurements to a cloud server. There is a rich literature on beehive monitoring. The choice of our work is not to use ready platforms such a...

Descripción completa

Detalles Bibliográficos
Autores principales: Rigakis, Iraklis, Potamitis, Ilyas, Tatlas, Nicolas-Alexander, Psirofonia, Giota, Tzagaraki, Efsevia, Alissandrakis, Eleftherios
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9921924/
https://www.ncbi.nlm.nih.gov/pubmed/36772447
http://dx.doi.org/10.3390/s23031407
_version_ 1784887428823121920
author Rigakis, Iraklis
Potamitis, Ilyas
Tatlas, Nicolas-Alexander
Psirofonia, Giota
Tzagaraki, Efsevia
Alissandrakis, Eleftherios
author_facet Rigakis, Iraklis
Potamitis, Ilyas
Tatlas, Nicolas-Alexander
Psirofonia, Giota
Tzagaraki, Efsevia
Alissandrakis, Eleftherios
author_sort Rigakis, Iraklis
collection PubMed
description We present a custom platform that integrates data from several sensors measuring synchronously different variables of the beehive and wirelessly transmits all measurements to a cloud server. There is a rich literature on beehive monitoring. The choice of our work is not to use ready platforms such as Arduino and Raspberry Pi and to present a low cost and power solution for long term monitoring. We integrate sensors that are not limited to the typical toolbox of beehive monitoring such as gas, vibrations and bee counters. The synchronous sampling of all sensors every 5 min allows us to form a multivariable time series that serves in two ways: (a) it provides immediate alerting in case a measurement exceeds predefined boundaries that are known to characterize a healthy beehive, and (b) based on historical data predict future levels that are correlated with hive’s health. Finally, we demonstrate the benefit of using additional regressors in the prediction of the variables of interest. The database, the code and a video of the vibrational activity of two months are made open to the interested readers.
format Online
Article
Text
id pubmed-9921924
institution National Center for Biotechnology Information
language English
publishDate 2023
publisher MDPI
record_format MEDLINE/PubMed
spelling pubmed-99219242023-02-12 A Low-Cost, Low-Power, Multisensory Device and Multivariable Time Series Prediction for Beehive Health Monitoring Rigakis, Iraklis Potamitis, Ilyas Tatlas, Nicolas-Alexander Psirofonia, Giota Tzagaraki, Efsevia Alissandrakis, Eleftherios Sensors (Basel) Article We present a custom platform that integrates data from several sensors measuring synchronously different variables of the beehive and wirelessly transmits all measurements to a cloud server. There is a rich literature on beehive monitoring. The choice of our work is not to use ready platforms such as Arduino and Raspberry Pi and to present a low cost and power solution for long term monitoring. We integrate sensors that are not limited to the typical toolbox of beehive monitoring such as gas, vibrations and bee counters. The synchronous sampling of all sensors every 5 min allows us to form a multivariable time series that serves in two ways: (a) it provides immediate alerting in case a measurement exceeds predefined boundaries that are known to characterize a healthy beehive, and (b) based on historical data predict future levels that are correlated with hive’s health. Finally, we demonstrate the benefit of using additional regressors in the prediction of the variables of interest. The database, the code and a video of the vibrational activity of two months are made open to the interested readers. MDPI 2023-01-27 /pmc/articles/PMC9921924/ /pubmed/36772447 http://dx.doi.org/10.3390/s23031407 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Rigakis, Iraklis
Potamitis, Ilyas
Tatlas, Nicolas-Alexander
Psirofonia, Giota
Tzagaraki, Efsevia
Alissandrakis, Eleftherios
A Low-Cost, Low-Power, Multisensory Device and Multivariable Time Series Prediction for Beehive Health Monitoring
title A Low-Cost, Low-Power, Multisensory Device and Multivariable Time Series Prediction for Beehive Health Monitoring
title_full A Low-Cost, Low-Power, Multisensory Device and Multivariable Time Series Prediction for Beehive Health Monitoring
title_fullStr A Low-Cost, Low-Power, Multisensory Device and Multivariable Time Series Prediction for Beehive Health Monitoring
title_full_unstemmed A Low-Cost, Low-Power, Multisensory Device and Multivariable Time Series Prediction for Beehive Health Monitoring
title_short A Low-Cost, Low-Power, Multisensory Device and Multivariable Time Series Prediction for Beehive Health Monitoring
title_sort low-cost, low-power, multisensory device and multivariable time series prediction for beehive health monitoring
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9921924/
https://www.ncbi.nlm.nih.gov/pubmed/36772447
http://dx.doi.org/10.3390/s23031407
work_keys_str_mv AT rigakisiraklis alowcostlowpowermultisensorydeviceandmultivariabletimeseriespredictionforbeehivehealthmonitoring
AT potamitisilyas alowcostlowpowermultisensorydeviceandmultivariabletimeseriespredictionforbeehivehealthmonitoring
AT tatlasnicolasalexander alowcostlowpowermultisensorydeviceandmultivariabletimeseriespredictionforbeehivehealthmonitoring
AT psirofoniagiota alowcostlowpowermultisensorydeviceandmultivariabletimeseriespredictionforbeehivehealthmonitoring
AT tzagarakiefsevia alowcostlowpowermultisensorydeviceandmultivariabletimeseriespredictionforbeehivehealthmonitoring
AT alissandrakiseleftherios alowcostlowpowermultisensorydeviceandmultivariabletimeseriespredictionforbeehivehealthmonitoring
AT rigakisiraklis lowcostlowpowermultisensorydeviceandmultivariabletimeseriespredictionforbeehivehealthmonitoring
AT potamitisilyas lowcostlowpowermultisensorydeviceandmultivariabletimeseriespredictionforbeehivehealthmonitoring
AT tatlasnicolasalexander lowcostlowpowermultisensorydeviceandmultivariabletimeseriespredictionforbeehivehealthmonitoring
AT psirofoniagiota lowcostlowpowermultisensorydeviceandmultivariabletimeseriespredictionforbeehivehealthmonitoring
AT tzagarakiefsevia lowcostlowpowermultisensorydeviceandmultivariabletimeseriespredictionforbeehivehealthmonitoring
AT alissandrakiseleftherios lowcostlowpowermultisensorydeviceandmultivariabletimeseriespredictionforbeehivehealthmonitoring