Estimation and Inference in Environment Sensing Networks


This project will develop inference tools that integrate large scale real time environmental data relevant to the future security of food and water.


Professor Salah Sukkarieh, Professor Liaquat Hossain, Professor Mathew Vadas, Professor Albert Y. Zomaya

Research Location

Charles Perkins Centre – the Judith and David Coffey Life Lab

Program Type



For continued availability of food and water, much future science in environment modeling will require access to real-time measurements of a variety of observables across large spatial and temporal domains. A number of large sensor networks are being developed to provide such measurements; of note are the NCRIS programmes such as the Integrated Marine Observation System (IMOS) and the Terrestrial Ecosystem Research Network (TERN), as well as international programmes such as the NSF Centre for Embedded Network Systems (CENS) Terrestrial Ecology Observing System (TEOS). A key challenge in these large-scale networks is to assimilate and merge the information obtained to allow estimation and inference about parameters and models of interest. This project will aim to explore the use of distributed Bayesian networks as underlying models for these large distributed measurement systems which provide a direct method of estimation and inference. The project will draw together science experts to provide domain knowledge for the inference process, statistical modelers and computer scientists to develop algorithms, such as those based on factor graphs and junction-tree techniques, for efficient computational approaches to these large-scale inference problems. The project will propose a small number of domains to develop these ideas; possibly on the basis of the information being produced by the large NCRIS projects. The outcome of the project will be in the form of large-scale real-time inference techniques and potentially a set of tools for building inference engines for large critical sensor networks

Additional Information

The Life Lab creates a new kind of graduate and postgraduate training environment at the interface between life, social, economic and physical sciences. Its focus is to address the significant challenges we face from an unsustainable food system that degrades the environmental services it depends on, and creates significant societal health problems. A better understanding of the complexity of the environment-food-health nexus is critical. It is fundamental to building a sustainable society, and one that is more robust to future uncertainties. Our unique approach will be a world-first in shifting research on these growing challenges from treating symptoms to prevention.Life Lab will challenge existing paradigms and university models to create a research training environment in which traditional disciplinary boundaries do not apply. Our ambitious vision is to create an ‘innovation hub' where researchers from disciplines spanning physical, life and social and economic sciences will interface with business, government and agency leaders. It will develop integrated approaches to the challenges that threaten societal wellbeing, and train the next generation of experts with the skills required to find solutions.

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Bayesian statistics, food security, water security, environmental monitoring, Distributed systems

Opportunity ID

The opportunity ID for this research opportunity is: 1687

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