Development of an heterogeneous wireless sensor network for instrumentation and analysis of beehives

Honey bees have held a critical role in agriculture and nutrition from the dawn of human civilisation. The most crucial role of the bee is pollination; the value of pollination dependant crops is estimated at € 155 billion per year with honey bees identified as the most important pollinator insect. It is clear that honey bees are a vitally important part of the environment which cannot be allowed to fall into decline. The project outlined in this paper uses Wireless Sensor Network (WSN) technology to monitor a beehive colony and collect key information about activity/environment within a beehive as well as its surrounding area. This project uses low power WSN technologies, including novel sensing techniques, energy neutral operation, and multi-radio communications; together with cloud computing to monitor the behaviour within a beehive. The insights gained through this activity could reduce long term costs and improve the yield of beekeeping, as well as providing new scientific evidence for a range of honey bee health issues. WSN is an emerging modern technology, key to the novel concept of the Internet of Things (IoT). Comprised of embedded sensing, computing and wireless communication devices, they have found applications in nearly every aspect of daily life. Informed by biologists' hypotheses, this work used existing, commercially available WSN platforms together with custom built systems in an innovative application to monitor honey bee health and activity in order to better understand how to remotly monitor the health and behaviour of the bees. Heterogeneous sensors were deployed, monitoring the honey bees in the hive (temperature, CO2, pollutants etc.). Weather conditions throughout the deployment were recorded and a relationship between the hive conditions and external conditions was observed. A full solution is presented including a smart hive, communication, and data aggregation and visualisation tools. Future work will focus on improving the energy performance of the system, introducing a more specialised set of sensors, implementing a machine learning algorithm to extract meaning from the data without human supervision; and securing additional deployments of the system.