b+WSN: Smart beehive for agriculture, environmental, and honey bee health monitoring - Preliminary results and analysis

• Published in: 2015 IEEE Sensors Applications Symposium (SAS) pp. 1 - 6
• Main Authors: Edwards Murphy, Fiona, Magno, Michele, Whelan, Padraig, Vici, Emanuel Popo
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.04.2015
• Subjects: Agriculture; Biological and Physical Sensors; Biology; Embedded Systems; Environmental Monitoring; Honey Bee Monitoring; Honey Bee Sensors; Humidity; Monitoring; Sensors; Weather Prediction; Wireless sensor networks
• DOI: 10.1109/SAS.2015.7133587

In recent years, various United Nations reports have stressed the growing constraint of food supply for Earth's growing human population. Honey bees are a vital part of the food chain as the most important pollinator insect for a wide range of crops. It is clear that protecting the population of honey bees worldwide, as well as enabling them to maximise their productivity, is an important concern. The work described in this paper utilised heterogeneous wireless sensor networks technologies to gather data unobtrusively from a beehive, describing the conditions and activity of the honey bee colony. A wide range of sensors were deployed for monitoring the multidimensional conditions within a living beehive (including oxygen, carbon dioxide, pollutant levels, temperature, and humidity). Meteorological and environmental conditions outside the hive were also monitored throughout the deployment. The data were then analysed from a biological perspective to provide insights into honey bee behaviour and health. This led to the development of an algorithm for automatically determining the status of the bee colony. Analysis was also undertaken from a meteorological perspective, which led to the development of an algorithm for predicting short term external weather conditions (rain) based on the conditions observed within the hive. The meteorological conditions were seen to have an impact on the data provided by biological sensors (bees) and physical sensors. This can be exploited to improve the accuracy of local weather prediction. Applications of this algorithm include agricultural and environmental monitoring for accurate short term forecasts for the area local to the beehive.