The Distributed Sensing Systems Group focuses its research on algorithms and methods that underpin large scale and long-term deployment of sensor networks to form an Internet of Things. More specifically, we address the problems of how to maximise information return from resource-constrained and distributed sensing devices. We solve these problems by applying our expertise in the following areas:
Energy-neutral Sensing: ensures that energy-constrained systems can match the energy input from harvesting sources is used to maximise information return while operating near-perpetually
Embedded intelligence is a key research area that aims to embed adaptive analytics and lightweight machine learning algorithms on resource constrained devices. It is primarily driven by the communications bandwidth and energy limitations of individual IoT devices.
Sensor modeling: uses the incoming sensor data to build models of the spatial and temporal dynamics of monitored processes, both to address an end user need and to feed back into optimising the operation of the system. A particular focus of this area is mobility mining: transforming large scale movement data from specialised and general platforms into meaningful movement models for prediction and optimisation
Trusted sensing: as more sensing devices are deployed in the world at large scale, the issues of trust, audibility, and traceability increase in importance. We tackle this problem through our blockchain for IoT security and privacy work.