Battery-free wildlife monitoring with Riotee

This is an idea proposed in 2025 as a good starter project, and is currently being worked on by Dominico Parish. It is co-supervised with Josh Millar.

Monitoring wildlife in the field today relies heavily on battery-powered devices, like GPS collars or acoustic recorders. However, such devices are often deployed in remote environments, where battery replacement and data retrieval can be labour-intensive and time-consuming. Moving away from battery-powered field devices could radically reduce the environmental footprint and labour cost of wildlife monitoring. The rise of batteryless energy-harvesting platforms could enable ultra-low-power, long-term, maintenance-free deployments. However, existing battery-less devices are severely constrained, often unable to perform meaningful on-device computation such as ML inference or high-frequency audio capture.

This project explores the development of next-generation, battery-less wildlife monitoring platforms using Riotee, an open-source platform purpose-built for intermittent computing. Riotee integrates energy harvesting with a powerful Cortex-M4 MCU and full SDK for managing state-saving, redundancy, and graceful resume from power failures.

The project could involve work on one or more of the following areas:

• SDK tooling: developing a user-friendly C/Rust SDK that integrates audio recording, ML-based data processing, scheduling, and wireless communication into a unified and easily configurable framework for non-technical users in conservation and ecology.
• GPS tracking: building a hardware/software solution using Riotee for wildlife tracking, harvesting energy from both motion and solar.
• Acoustic monitoring: exploring the feasibility of bioacoustic monitoring on Riotee, quantifying the trade-off between scalability/lifetime and ecological data yield.
• On-device ML: adapting or training lightweight ML models to fit within Riotee’s memory and energy budgets, and intermittent compute runtime.

This project would suit a student interested in low-power hardware and/or applied ML. Prior experience with C and embedded programming would be helpful, but the desire to get your hands dirty with low-level debugging is essential!