Reduced Energy and Memory Requirements by On-Board Behavior Classification for Animal-Borne Sensor Applications

Abstract

The ability to study animal behavior is important in many fields of science, including behavioral ecology, conservation, and precision farming. These studies typically employ biotelemetry tags attached to animals that collect raw sensor data from tri-axial accelerometers and global positioning system modules. The lifespan of such tags is constrained by their power and memory usage, which are often limiting factors when performing behavioral studies for extended periods of time. This paper considers the power requirement and memory usage benefits of performing statistical behavior classification on the tag itself, as opposed to at a receiver station after raw data transmission. Experiments using specially designed low-power biotelemetry sensors demonstrated a 27-fold reduction in energy consumption and a 469-fold reduction in memory usage when classification was performed on the tag, rather than after raw data transmission. By performing on-board statistical behavior classification, both the power requirements and the memory usage are drastically reduced, thereby prolonging the lifespan of the tag.