Theme 1
Sensors, Placement & Operation
Advance promising sensor technologies along the TRL pathway for quicker industry implementation while minimizing operational costs and risks through non-contact sensing and optimized deployment strategies.
About the Project
The Sensing, Placement & Operation theme focuses on advancing the next generation of water-quality and environmental sensing technologies.This includes developing innovative biosensors, ML/AI-enhanced devices, and smart energy-harvesting systems that can monitor emerging contaminants such as ammonia, heavy metals, and pathogens. By combining non-contact, optical, and biosensing technologies, we aim to deliver high-resolution, real-time data for improved understanding of water systems—without the need for direct physical sampling.
A second pillar of this theme focuses on how sensors are deployed, positioned, and integrated into real-world environments. This includes designing novel autosampling systems, optimizing sensor density and placement strategies, and addressing operational uncertainties such as tides, weather fluctuations, and environmental disturbances. Through advanced modelling, observability-based placement algorithms, drone and satellite sensing integration, and strategic deployment frameworks, we aim to maximise data quality, minimise operational risks, and ensure long-term reliability of monitoring systems across diverse catchments and water infrastructure.
Project Objectives
Advanced Sensor Innovation
Develop reliable, low-cost, and low-power sensing technologies capable of measuring key water-quality parameters with improved accuracy and long-term performance.
Non-Contact Monitoring
Leverage optical, drone, and satellite sensing systems to capture water quality and hydrological data without physical contact, enabling safer and broader monitoring coverage.
Smart & Efficient Sampling
Design compact, energy-efficient autosamplers that provide on-demand sampling in sites where traditional sensors are limited, ensuring timely and reliable sample collection.
Optimized Sensor Deployment
Use modelling, analytics, and placement algorithms to identify ideal sensor locations and densities, improving data quality across diverse catchments and water systems.
Resilient Field Operation
Enhance sensor performance by reducing the impact of tides, weather shifts, environmental disturbances, and site conditions, ensuring stable and consistent data collection.
Intelligent Analytics & Power Management
Integrate machine learning, automation, and energy-harvesting technologies to boost sensing efficiency, streamline maintenance, and support sustainable long-term monitoring.
Key Milestones
No milestones available
Meet Our Visionary Team
Dr Baiqian (Luke) Shi
QUT Node Lead
Dr. Baiqian (Luke) Shi completed his PhD in Civil Engineering from Monash University in 2022, focusing on urban stormwater management. His research interests include real-time monitoring and control o...