Where should we put our sensors? Designing smarter water quality monitoring networks

Overview

Water utilities and environmental agencies face a deceptively simple question: where should we put our sensors? In a piped stormwater network, the question matters because finding an illicit discharge quickly depends on having the right sensors at the right places. In an open creek or river, the question matters because spatial coverage, transport dynamics, and cost trade-offs all influence whether monitoring will actually answer the question being asked.

Decisions about sensor placement are still often made on the basis of accessibility, history, or rule-of-thumb. As the IoT Water Hub develops new generations of low-cost sensors and smart samplers, partners need clear, defensible rules for choosing where to put them, how many to deploy, and what sensor performance is needed for each monitoring objective.

This PhD will develop an integrated framework for designing sensor and sampler networks for two anchoring cases: illicit discharge detection in piped stormwater systems, and surveillance of creeks and rivers.

Research activities

Working as part of the IoT for Water Hub (an ARC Industrial Transformation Research Hub), you will:

• work with partners to define monitoring objectives and sensor performance requirements for the two cases
• build forward modelling frameworks for piped-network and open-channel transport, integrating real sensor performance characteristics drawn from sister projects
• develop network optimisation tools that solve the trade-off between sensor quality and density
• run field validation deployments at multiple Hub case-study sites.
• develop an adaptive operational framework for refining networks as conditions change over time
• produce two case-specific design guides for partner adoption.

You will be supported by a senior Research Fellow and a Research Assistant providing field deployment and modelling capacity.

Outcomes

By the end of the PhD you will have developed a validated framework for designing water quality monitoring networks under realistic operational and budget constraints, with field evidence from multiple Hub sites and clear partner-facing design guides. You will graduate with skills spanning hydraulic and water quality modelling, optimisation, machine learning, and applied data science, with strong career paths into water utilities, consultancies, environmental regulators, and digital water companies.

Skills and experience

We are looking for a student with a Bachelor (Honours) or Masters degree in environmental engineering, civil engineering, computer science, mathematics, or a related discipline. Experience or strong interest in hydraulic or water quality modelling, optimisation, machine learning, or data-driven decision systems is highly desirable. The student should be comfortable with both quantitative modelling and field engagement with industry partners.

Scholarships

You may be eligible to apply for a research scholarship.

Explore our research scholarships

Keywords

• sensor placement
• monitoring
• optimisation
• machine learning
• IoT

Contact

Contact the supervisor for more information