Efsevia Tzagaraki
Research Subjects
Research Interests
Efsevia Tzagaraki has a total of 5 co-authors. Here are the top co-authors by number of shared publications:
- Eleftherios Alissandrakis
Department of Agriculture, School of Agricultural Sciences, Hellenic Mediterranean University, Estavromenos, 71004 Heraklion, Greece
1 shared publication - Giota Psirofonia
Department of Music Technology & Acoustics, Hellenic Mediterranean University, 74100 Rethymno, Crete Island, Greece
1 shared publication - Ilyas Potamitis
Hellenic Mediterranean University, Greece
1 shared publication - Iraklis Rigakis
Department of Electrical and Electronics Engineering, University of West Attica, 12244 Athens, Greece
1 shared publication - Nicolaos-Alexandros Tatlas
Department of Electrical and Electronics Engineering, University of West Attica, 12244 Athens, Greece
1 shared publication
Latest Publications
- Open Access
- 21 December 2022
- Published by MDPI AG in Preprints.org
A Low-cost, Low-power, Multisensory Device and Multivariable Timeseries 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 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 minutes allows us to form a multivariable timeseries that serves 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.
- 21 December 2022
- Published by MDPI AG in Preprints.org
Open AccessA Low-cost, Low-power, Multisensory Device and Multivariable Timeseries 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 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 minutes allows us to form a multivariable timeseries that serves 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.