Project background
The demand and use of tracking devices to collect information remotely and with minimum supervision has increased exponentially in recent decades. Among the applications the tracking of animals and environmental data with sensors has been revolutionised due to new technological advances that enable the acquisition of high spatial and temporal resolution data. Tracking devices, its associated sensors and the high-resolution data obtained have opened a new frontier and enabled researchers to answer new questions in environmental science, geophysics, animal ecology, evolution and physiology.
The emergence of technologies for Internet of Things (IoT) brings significant advances and benefits to studies that require remotely acquired data. At UEA, in collaboration with a Swiss based company and Movetech Telemetry, we developed a new tracking system that uses Long Range (LoRa) data transmission to send GPS location, acceleration, magnetometer and gyroscope information. It uses a high-performance wireless mesh, fully bi-directional and remotely programmable which makes it ideal for Internet of Things (IoT) and machine-to-machine (M2M) applications (e.g. smart buildings). It is optimized for low power consumption, hence low weight devices can be assembled which is ideal for small animal tracking.
This REP project will be done in collaboration with Movetech Telemetry https://movetech-telemetry.com/, the Universities in Lisbon and Porto, the British Trust for Ornithology and the electronics workshop at UEA. The REP student will join this collaborative team and will have the opportunity to discuss the methods and results with the team. The student will be given access to the firmware on the devices and will learn to programme and test the firmware and software. The project includes firmware implementation and experimentation.
The software controls the parameters of data acquisition on the device contained within the transmission which will determine the budget and time-on-air. By optimising this, we can maximise the battery life versus in a range of environments (e.g. forested areas or off-shore wind farms). At present the device does not enable different patterns of data acquisition with time of day and we want to change this feature and test it. We envision 3-4 weeks for firmware implementation and 2-3 weeks of tests and communication of the results with the multidisciplinary team.