Background: With the global decline of honey bee populations, safeguarding these vital pollinators has become crucial. Varroa destructor mites are a primary threat, weakening bees and facilitating the spread of diseases, which can decimate colonies and disrupt ecosystems. This thesis investigates the application of a Wireless sensor network (WSN) for the monitoring and control of varroasis spread within bee colonies across large geographical areas. Objectives: The main objective of this research is to develop an integrated method that combines biological insights into varroasis with WSN functionalities for real-time disease monitoring and control. By doing so, the study aims to contribute to the development of a scalable and sustainable approach to apiculture and disease management. Methods: A multi-phase methodological approach was employed, encompassing the modelling of biological phenomena, formulation of WSN functionalities, and the design of a scalable WSN architecture. Simulation studies were conducted, followed by the development of a theoretical framework to support the practical application of the proposed WSN system. A key aspect of the methodology is the introduction of energy estimation models to evaluate the operational feasibility of the WSN. Results: The results indicate that the WSN is capable of dynamically adjusting its monitoring rate in response to changes in infection dynamics, effectively identifying and managing varroa mite populations. The system demonstrated adaptability to various infection rates, with the potential to improve the timely and targeted treatment of infested colonies. Energy consumption data further affirms the operational viability of the WSN. Conclusions: The study concludes that integrating WSNs with biological models is a viable solution for the real-time monitoring and management of varroasis. The proposed WSN system holds promise for enhancing the health and productivity of bee colonies on a broad scale, offering a novel contribution to the fields of apiculture and environmental monitoring.

This master thesis is exploring Beekeepers usage of Internet of Things, or “Internet of Bees”. Since most of the prior contributions are focusing on data gathering, the approach to focus on the users needs is central to take next steps in the field of using IoT for Beekeeping. After the introduction a chapter with an overview of current research and commercial solutions are presented. This is followed by a quantitative study with 222 responds, answering what beekeepers like to know about their bees, what platforms used by end users and what the beekeeper as a user expects. An demo of an existing commercial system is set up in real conditions, describing how to mount and configure a demo. Communication, synchronization and presentation is described. A closed user interface and a public user interface are a part of the demonstration. Potential users of this technique are interviewed to gain better understanding of users opinion of the demo. This is followed by another demo using a free of charge app where sound analysis processed with AI is tested. This thesis explains what beekeepers as users of Internet of Things could gain added value to their beekeeping.