Linkage Infrastructure, Equipment And Facilities - Grant ID: LE0883073
Funder
Australian Research Council
Funding Amount
$200,000.00
Summary
BigNet - A Distributed Wireless Sensor Network Testbed. The infrastructure developed will be of national /international significance, given the rapid emergence of wireless sensor networks. This integrated facility will allow Australia to be a world leading player in the research and technology development as well as the socially responsible deployment of sensor networks. The facility has the explicit aim to ensure that Australia is a technology leader rather than solely a technology user in sens ....BigNet - A Distributed Wireless Sensor Network Testbed. The infrastructure developed will be of national /international significance, given the rapid emergence of wireless sensor networks. This integrated facility will allow Australia to be a world leading player in the research and technology development as well as the socially responsible deployment of sensor networks. The facility has the explicit aim to ensure that Australia is a technology leader rather than solely a technology user in sensor networks. The test facility will mirror practical requirements for WSN implementation in the Great Barrier Reef and in timber plantation, which would offer substantial economic benefits to Australia.Read moreRead less
Data-driven water quality treatment management decision support system. Data-driven water quality treatment management decision support system. This project aims to develop a robust decision support system to predict manganese and the character and concentration of dissolved organic matter in drinking water reservoirs, using intelligent algorithms and data collected through remote autonomous instrumentation. These predicted water quality parameters could be used as model input variables to provi ....Data-driven water quality treatment management decision support system. Data-driven water quality treatment management decision support system. This project aims to develop a robust decision support system to predict manganese and the character and concentration of dissolved organic matter in drinking water reservoirs, using intelligent algorithms and data collected through remote autonomous instrumentation. These predicted water quality parameters could be used as model input variables to provide real-time decisions for plant operators on the required treatment regime for incoming raw water, and advise them on the optimal reservoir offtake depth. This will potentially minimise treatment costs and health risks for consumers. The ultimate goal is to significantly enhance current water supply management practices.Read moreRead less