Decision support system for temperature monitoring in beehives

Abstract

European honeybee colonies are the most important pollinator insects and source of honey and other useful products. Honeybee colonies today face new diseases and pests as well as pollution which threaten their survival and endanger whole food production which relies on honey bee pollination. Internet of Things (IoT) technology enables integration of wireless sensors inside beehives to enable remote monitoring of various beehive parameters from remote location using Internet. Detection of certain critical events in beehive is hard to be explicitly program due to complex dependence between multiple input parameters. Machine learning algorithms give computers the ability to learn to detect these events without being explicitly programmed. Detection of these event from streams of data collected from IoT sensors is possible using Complex Event Processing (CEP) which applies machine induced knowledge do detect and warn beekeepers about certain events in beehive.