Research library

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 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.

Publication details

Authors
Igor Kurdin, Aleksandra Kurdina
Organizations
🇵🇱 Warsaw University of Life Sciences
Year
2024
Type
Journal

Relevancy to Gratheon

This paper is relevant to Gratheon because it informs sensor hardware, telemetry pipelines, and monitoring dashboards. Its methods and findings can be translated into product requirements for reliable field deployments: what should be sensed, how signals should be interpreted, and which uncertainty or validation limits need to be surfaced to beekeepers. For Gratheon, the work is most useful as an evidence-backed design reference for connecting local hive observations with actionable recommendations in the web app while keeping hardware practical for remote apiaries.