Wildlife studies are often marred with problems of accessibility and observability constraining data collection. However, with the advent of machine learning and, more recently still, edge computing, there is increasing potential for a step-change in elucidating the ecology of the globe’s wildlife.
Most ecological research follows a conventional data collection and processing paradigm, where raw data is first collected (for example by using bio-logging devices or camera traps) and then processed post-hoc (using a personal computer or server). Edge computing is a distributed computing paradigm that brings computation and data storage closer to the data sources. The main features of this computing architecture are reductions in latency and bandwidth use for data transmission.
The project aims to apply state-of-the-art machine learning algorithms (deep learning) on board sensory platforms to achieve increased, richer data collection at the source, better automation in ecological monitoring, and faster responses to disrupting events, such as weather conditions or disease transmission.
A literature review entitled “Edge computing in wildlife behaviour and ecology” is under review in a scientific journal. In collaboration with the Wildlife Ecology and Conservation Group, the researchers of this project are testing the performance of a deep-learning framework on behaviour classification of multiple animal species using accelerometer data. This deep-learning framework can be configured on-board of animal tracking devices for automatic and long-term behavioural monitoring.