Internet of Things Smart Beehive Network: Homogeneous Data, Modeling, and Forecasting the Honey Robbing Phenomenon
The role of experimental data and the use of IoT-based monitoring systems are gaining broader significance in research on bees across several aspects: bees as global pollinators, as biosensors, and as examples of swarm intelligence. This increases the demands on monitoring systems to obtain homogene...
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MDPI AG
2025-03-01
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| Series: | Inventions |
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| Online Access: | https://www.mdpi.com/2411-5134/10/2/23 |
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| author | Igor Kurdin Aleksandra Kurdina |
| author_facet | Igor Kurdin Aleksandra Kurdina |
| author_sort | Igor Kurdin |
| collection | DOAJ |
| description | The role of experimental data and the use of IoT-based monitoring systems are gaining broader significance in research on bees across several aspects: bees as global pollinators, as biosensors, and as examples of swarm intelligence. This increases the demands on monitoring systems to obtain homogeneous, continuous, and standardized experimental data, which can be used for machine learning, enabling models to be trained on new online data. However, the continuous operation of monitoring systems introduces new risks, particularly the cumulative impact of electromagnetic radiation on bees and their behavior. This highlights the need to balance IoT energy consumption, functionality, and continuous monitoring. We present a novel IoT-based bee monitoring system architecture that has been operating continuously for several years, using solar energy only. The negative impact of IoT electromagnetic fields is minimized, while ensuring homogeneous and continuous data collection. We obtained experimental data on the adverse phenomenon of honey robbing, which involves elements of swarm intelligence. We demonstrate how this phenomenon can be predicted and illustrate the interactions between bee colonies and the influence of solar radiation. The use of criteria for detecting honey robbing will help to reduce the spread of diseases and positively contribute to the sustainable development of precision beekeeping. |
| format | Article |
| id | doaj-art-ca8e3d23b7e541b6b1ee8adb39d3eccf |
| institution | OA Journals |
| issn | 2411-5134 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Inventions |
| spelling | doaj-art-ca8e3d23b7e541b6b1ee8adb39d3eccf2025-08-20T02:18:16ZengMDPI AGInventions2411-51342025-03-011022310.3390/inventions10020023Internet of Things Smart Beehive Network: Homogeneous Data, Modeling, and Forecasting the Honey Robbing PhenomenonIgor Kurdin0Aleksandra Kurdina1Department of Artificial Intelligence, Institute of Information Technology, Warsaw University of Life Sciences, Nowoursynowska 159, 02-776 Warsaw, PolandIndependent Researcher, 02-627 Warsaw, PolandThe role of experimental data and the use of IoT-based monitoring systems are gaining broader significance in research on bees across several aspects: bees as global pollinators, as biosensors, and as examples of swarm intelligence. This increases the demands on monitoring systems to obtain homogeneous, continuous, and standardized experimental data, which can be used for machine learning, enabling models to be trained on new online data. However, the continuous operation of monitoring systems introduces new risks, particularly the cumulative impact of electromagnetic radiation on bees and their behavior. This highlights the need to balance IoT energy consumption, functionality, and continuous monitoring. We present a novel IoT-based bee monitoring system architecture that has been operating continuously for several years, using solar energy only. The negative impact of IoT electromagnetic fields is minimized, while ensuring homogeneous and continuous data collection. We obtained experimental data on the adverse phenomenon of honey robbing, which involves elements of swarm intelligence. We demonstrate how this phenomenon can be predicted and illustrate the interactions between bee colonies and the influence of solar radiation. The use of criteria for detecting honey robbing will help to reduce the spread of diseases and positively contribute to the sustainable development of precision beekeeping.https://www.mdpi.com/2411-5134/10/2/23precision apiculturewireless sensor networksmart hivesolar energymachine learningIoT |
| spellingShingle | Igor Kurdin Aleksandra Kurdina Internet of Things Smart Beehive Network: Homogeneous Data, Modeling, and Forecasting the Honey Robbing Phenomenon Inventions precision apiculture wireless sensor network smart hive solar energy machine learning IoT |
| title | Internet of Things Smart Beehive Network: Homogeneous Data, Modeling, and Forecasting the Honey Robbing Phenomenon |
| title_full | Internet of Things Smart Beehive Network: Homogeneous Data, Modeling, and Forecasting the Honey Robbing Phenomenon |
| title_fullStr | Internet of Things Smart Beehive Network: Homogeneous Data, Modeling, and Forecasting the Honey Robbing Phenomenon |
| title_full_unstemmed | Internet of Things Smart Beehive Network: Homogeneous Data, Modeling, and Forecasting the Honey Robbing Phenomenon |
| title_short | Internet of Things Smart Beehive Network: Homogeneous Data, Modeling, and Forecasting the Honey Robbing Phenomenon |
| title_sort | internet of things smart beehive network homogeneous data modeling and forecasting the honey robbing phenomenon |
| topic | precision apiculture wireless sensor network smart hive solar energy machine learning IoT |
| url | https://www.mdpi.com/2411-5134/10/2/23 |
| work_keys_str_mv | AT igorkurdin internetofthingssmartbeehivenetworkhomogeneousdatamodelingandforecastingthehoneyrobbingphenomenon AT aleksandrakurdina internetofthingssmartbeehivenetworkhomogeneousdatamodelingandforecastingthehoneyrobbingphenomenon |