Page tree

This site is now obsolete. Please see Cyber Physical Systems

Skip to end of metadata
Go to start of metadata

You are viewing an old version of this page. View the current version.

Compare with Current View Page History

« Previous Version 5 Next »

We have seen an exponential increase in data flow and availability from large numbers of sensors. The DSS Group at CSIRO works on using this data to create models of the underlying sensed phenomena in order to more effectively monitor these phenomena in the future. 

Spatiotemporal Modeling of Animal Movement for Optimal Sampling of GPS positions

 

Accurate and energy-efficient location tracking of mobile objects is an important component of context-aware services and applications. While GPS receivers offer high accuracy positioning, energy constraints of battery powered devices necessitate duty-cycling of GPS to prolong the system life- time. Energy harvesting can extend the life of tracking appli- cations to a near perpetual operation. However, if movement patterns and energy resources change frequently, GPS sam- pling needs to adapt in real-time to achieve optimal position- ing performance. This work uses  energy- and mobility- aware scheduling framework for sampling of GPS in long- term tracking applications. 

 


 

Spatiotemporal Modelling by Fusing Sensor Data with Existing Data Sources

 

Conventional monitoring of physical spaces uses manual sample collection to build coarse-grained spatiotemporal models of monitored phenomena. With the increased availability and affordability of low power sensors, using data from these sensors to validate and evolve existing models presents both a challenge and an opportunity. Conventional methods have undergone long-term testing yet they only provide coarse-grained information, while low power sensor networks are relatively new yet provide high resolution spatial and temporal data. The DSS Group is working on new methods for fusing the different data sources from sensor networks and conventional methods for more reliable and representative spatiotemporal models of the underlying environment. We have already applied this capability to the problem of mine rehabilitation, where mining companies must maintain a lease on the mine site until they can show they have restored the ecosystem to its original state.

 

 

Publications

K. Zhao, R. Jurdak, J. Liu, D. Westcott, B. Kusy, H. Parry, P. Sommer, A. McKeown, ''Optimal Lévy-flight foraging in a finite landscape", Journal of the Royal Society Interface 12, 20141158 January 2015.

R. Jurdak, K. Zhao, J. Liu, M. AbouJaoude, M. Cameron, D. Newth, “Understanding Human Mobility from Twitter,” Accepted at PLOS ONE, June, 2015.

B. Kusy, C. Richter, S. Bhandari, R. Jurdak, M. Ngugi, G. Nelder, “Evidence-based Landscape Rehabilitation through Microclimate Sensing,” In proceedings of the Twelfth Annual IEEE International Conference on Sensing, Communication, and Networking (SECON), Seattle, USA, June 2015.

I. Purnama, R. Jurdak, K. Zhao and N. Bergmann, “Characterising and Predicting Urban Mobility Dynamics By Mining Bike Sharing System Data,” To appear in proceedings of 12th IEEE International Conference on Ubiquitous Intelligence and Computing (UIC), Bejing, China, August 2015.

 J. Liu, K. Zhao, S. Khan, M. Cameron, R. Jurdak, “Multi-scale Population and Mobility Estimation with Geo-tagged Tweets,” In proceedings of 31st IEEE International Conference on Data Engineering (ICDE) Workshop BioBAD 2015, Seoul Korea, April 2015.

  • No labels