Sensor data is critical for the evaluation of the status of the physical world. Many sensors in use today, especially in agricultural contexts, are not suitable for long-term and mass-scale deployment when used for tracking progress in remote natural environments (such as forests). This is because forest environments are less controlled and consistent than agricultural ones, and access to deployed sensors is usually quite limited. Thus, the sensors need to be self-managing, robust and long lived, and degrade well beyond their end of life, i.e., not leave behind a toxic residue to pollute the soil.
Sensors may make different trade-offs between energy usage and lifespan. For example, in the context of reforestation, active medium-lifespan sensors (e.g., spectroscopy for measuring soil quality) used during sapling establishment could be deployed alongside passive long-lived sensors designed to last for the expected lifetime of the trees (e.g., temperature and humidity via chipless RFID). Additional sensors will need to be considered outside of those normally associated with agriculture, for example to account for dead organic matter, estimate carbon content of soil and litter, and to monitor for poaching, theft, vandalism, and possible fraud. Thus, there is ample scope for research into appropriate sensor system design.
Once the sensors are deployed, we must also consider how to integrate the diverse spatial and temporal field/point measurements of biodiversity and ecosystem functioning with remote sensing data. This project will therefore additionally investigate how to integrate the results from ground-based sensors to establish ground truth for remote sensing imagery, and conversely how to use remote sensing to tie together ground based sensors.
Useful skills for this project include:
- Embedded system software programming (e.g. in C, Rust, or OCaml)
- Hardware peripheral design for sensor probes
- Wireless baseband designs (e.g. audio, ultrasound, NFC, Bluetooth)
The successful candidate will join the Application of Artificial Intelligence to the study of Environmental Risks (AI4ER) Centre for Doctoral Training (CDT) based at the University of Cambridge. The AI4ER CDT programme consists of a one-year Master of Research (MRes) course (two terms of formal teaching via lectures, practicals and team challenges plus a three-month research project), followed by a 3 year PhD project. Both the masters and PhD project will be based on the above project description.
For further details on this project and how to apply please visit AI4ER’s applying to us webpages