Telstra-UOW Hub for AIOT Solutions

Call for Applications: Telstra-UOW Hub for AIOT Solutions Industry Collaboration Funding

The Telstra-UOW Hub for AIOT Solutions is excited to announce the launch of our Industry Collaboration Funding Program. This initiative supports research and development in AIOT technologies to drive innovation and enhance productivity in the Illawarra region. Our goals include fostering job creation and increasing international market value through strategic partnerships and cutting-edge projects.

Key Details:

  • Funding Amounts:
    • Grants range from $10,000 to $40,000.
    • Funding is contingent upon matching contributions from Industry Collaborators.
    • Higher cash contributions qualify for larger grants.
  • Eligibility:
    • Open to Australian organisations and registered non-profits.
    • Projects must align with the strategic priorities of the University of Wollongong.
    • Projects should contribute to regional development and advance AIOT technologies.
  • Timeline:
    • Applications open: 7 June 2024.
    • Applications close: 5 July 2024.
    • Notification of outcomes: 12 July 2024.
  • Additional Information:
    • Detailed guidelines and the application form are available below.
    • For questions regarding eligibility or application procedures, contact Hub Manager Tim Davies at

Join us in driving technological innovation and economic growth in the Illawarra region. Apply for the Telstra-UOW Hub for AIOT Solutions Industry Collaboration Funding Program today!

Telstra-UOW Hub for AIOT Solutions Industry Collaboration Funding Application

Telstra-UOW Hub for AIOT Solutions Industry Collaboration Funding Guidelines



Completed and Current projects

In response to Australia's rising challenge of recyclable contamination, this project has developed an innovative solution leveraging advanced computer vision and deep learning techniques. Partnering with the Wollongong City Council and REMONDIS, the project aimed to automate the detection of contaminants in recyclables, freeing truck drivers from manual monitoring tasks. By analysing real-time video footage from bin-emptying processes, implemented deep neural network algorithm identified various plastic bag contaminants, providing precise location-based data. This transformative approach not only streamlines waste management operations but also empowers targeted interventions to enhance recycling efficacy and reduce landfill contributions.


Remondis garbage truck, project equipment, garbage in hopper Remondis Edge computer setup
Remondis Architecture multiple pictures of examples of detections in the hopper

Related Publication

  1. Iqbal, U., Barthelemy, J., Perez, P. and Davies, T., 2022. Edge-computing video analytics solution for automated plastic-bag contamination detection: a case from remondis. Sensors, 22(20), p.7821.



Floodborne debris poses significant challenges during flooding events, often leading to infrastructure damage and altered flow dynamics. Traditional flood models struggle to account for debris impacts, largely due to the complex nature of debris movement and lack of automated identification solutions. Partnering with AVCON, our initiative harnesses cutting-edge computer vision and deep learning techniques to develop a system capable of detecting and classifying various debris types from video footage. Leveraging state-of-the-art neural network architectures and transfer learning methodologies, our solution aims to provide real-time insights, aiding flood management agencies in proactive mitigation strategies.


Related Publication

  1. Iqbal, U., Riaz, M.Z.B., Barthelemy, J., Hutchison, N. and Perez, P., 2022. Floodborne Objects Type Recognition Using Computer Vision to Mitigate Blockage Originated Floods. Water, 14(17), p.2605.

Successfully addressing the challenges of blockages at cross-drainage hydraulic structures (e.g., culverts), StopBlock implemented and developed innovative AI-oriented solutions. By harnessing state-of-the-art AIoT technology, including cutting-edge computer vision and edge computing powered by NVIDIA Jetson, our solution provides real-time assessment of culvert blockage statuses. Through the integration of advanced deep learning classification, detection and segmentation models, the project achieved precise detection and classification of blockage, ensuring timely alerts and optimised flood management strategies. Committed to privacy and transparency, the system transmitted only metadata, upholding data compliance while bolstering infrastructure resilience. Furthermore, the project has pioneered the creation of a comprehensive culvert dataset, contributing to collaborative advancements in stormwater management practices.

stopblock equipment


Related Publications

  1. NVIDIA Technical Blog --
  2. Iqbal, U., Barthelemy, J., Li, W. and Perez, P., 2021. Automating visual blockage classification of culverts with deep learning. Applied Sciences, 11(16), p.7561.
  3. Barthelemy, J., Amirghasemi, M., Arshad, B., Fay, C., Forehead, H., Hutchison, N., Iqbal, U., Li, Y., Qian, Y. and Perez, P., 2020. Problem-driven and technology-enabled solutions for safer communities: The case of stormwater management in the illawarra-shoalhaven region (nsw, australia). Handbook of smart cities, pp.1-28.
  4. Iqbal, U., Barthelemy, J., Perez, P., Cooper, J. and Li, W., 2023. A scaled physical model study of culvert blockage exploring complex relationships between influential factors. Australasian Journal of Water Resources, 27(1), pp.191-204.
  5. Iqbal, U., Barthelemy, J. and Perez, P., 2022. Prediction of hydraulic blockage at culverts from a single image using deep learning. Neural Computing and Applications, 34(23), pp.21101-21117.
  6. Iqbal, U., Bin Riaz, M.Z., Barthelemy, J. and Perez, P., 2022. Prediction of hydraulic blockage at culverts using lab scale simulated hydraulic data. Urban Water Journal, 19(7), pp.686-699.
  7. Iqbal, U., Bin Riaz, M.Z., Barthelemy, J. and Perez, P., 2023. Quantification of visual blockage at culverts using deep learning based computer vision models. Urban Water Journal, 20(1), pp.26-38.
  8. Iqbal, U., Bin Riaz, M.Z., Barthelemy, J. and Perez, P., 2023. Artificial Intelligence of Things (AIoT)-oriented framework for blockage assessment at cross-drainage hydraulic structures. Australasian Journal of Water Resources, pp.1-11.



Clearlight Saunas, a manufacturer of infrared saunas, have approached the Hub seeking capabilities to create infrastructure surrounding capturing data related to physiological parameters of sauna users and implementing Artificial Intelligence to determine possible health-benefits of sauna use. This project was completed 2023. The project delivered:
• An evaluation of commercially available sensors suitable for measuring environmental and physiological data during sauna use;
• Development of custom prototype sensors where required to supplement commercially available sensors;
• Creation of a data system to take raw sensor data into a consumer-facing dashboard or alert system; and
• Integration of the above mentioned system into a prototype sauna.

The first phase of the HE Silo project is almost complete. The project addresses a pivotal challenge globally, and in regional Australia's grain industry, which contributes over $13 billion annually but faces up to 5% grain spoilage due to post-harvest degradation in silos.

Globally the Post harvest degradation is reported as high as 30% in the emerging countries, where protecting the harvested grains is extremely important for feeding the world, even a 1% improvement to Post Harvest losses, would and many millions of tonnes of available grains for consumption back into the system.

The primary cause is moisture and temperature fluctuations from inadequate venting, leading to spoilage issues like mould, insect infestations, and even combustion. To counter this, a prototype autonomous sensing and actuation system is being developed to mitigate moisture buildup in silos, aiming to revolutionise grain storage and reduce spoilage.

The smart venting solution autonomously detects and addresses moisture buildup, enhancing production efficiency and potentially unlocking significant economic benefits. Significant progress has been made, with a developed electronic and control system capable of intelligently managing a system to combat moisture buildup, alongside implementing LoRaWAN technology for real-time monitoring.

Efforts are underway to extend sensor lifespan through SMART power management. A second phase of this project is in development, with plans to implement the system in a full-size grain silo to further test and advance the solution.

The Technology and Women’s Safety project is part of the iMove CRC. This research is to assess the opportunity to detect behaviours that threaten or imply a threat to women’s safety and develop tools that do so.

The project is being led by the Queensland University of Technology (QUT) with UOW (through SMART) a collaborating party. The final report was delivered in December 2023.

This project aimed to alleviate commuter frustrations due to peak-hour road congestion, offering a more streamlined and effective public transport system.

The project culminated in a detailed report with five key recommendations:

• Development of an Integrated Multi-Modal Services Plan for the Illawarra that includes seamless interchange between modes
• Customer-centred design thinking should be utilised to develop the Services Plan, which prioritises an intra-regional Illawarra commuter focus
• Implementation of the Illawarra Integrated Multi-Modal Services Plan, including changes to the current operator service plan
• Provision of Service-driven infrastructure improvements to support the Illawarra Integrated Multi-Modal Services Plan implementation, such as:
Additional East West Link for Shellharbour City Centre Hub
• Additional M1 alignment in Services Plan for Wollongong CBD Commute Hub Integrated ticketing system across all transport modes, utilising Opal-enabled services.

The full report, and presentation that was delivered to key stakeholders can be found here:

There was some media generated on this project with local radio and newspapers picking it up


The iOyster project is a collaboration between SMART, Oceanfarmr and NSW Department of Primary Industries.

We are using sensor technology in oyster grow-baskets to de-risk oyster farming. The device will provide early detection of harmful heat and biofouling to help prevent catastrophic losses in periods of extreme heat and drought.

Funded by the Federal Future Drought Fund.

There is growing interest in biodiversity credits, but aquaculture does not yet have a way to collect evidence for investors.

This project is developing a methodology and prototype system of low-cost underwater cameras, water quality instruments and AI for gathering evidence of biodiversity on oyster farms. This will help farmers to attract funding in return for the biodiversity they support.

Funded by the Federal Innovation Connections Programme.

The Digital twin project leverages Simulation Modelling and advanced analytics to optimise target objectives. It aims to explore new scenarios and assess various business decisions, using a developed simulation-optimisation framework.

As models grow complex, traditional trial and error methods for parameter tuning become inefficient. The proposed system, modelled as a Digital Twin, incorporates live IoT data for intelligent, data-driven analysis.

Utilising reinforcement learning and software like AnyLogic, alongside reviewing metaheuristic optimisation algorithms, the solution aims to optimise Blast Furnace 5 stock house operations at BlueScope steel, focusing on aspects like discharge rates and bin capacities to enhance efficiency and minimise material degradation.

Further, integration with Telstra Digital Twin and other AI/ML services is explored to augment the platform's predictive analytics capabilities, aiming for live, production-ready deployment in the project's second phase.

Seed Funding Program

Informing the development of AIOT-powered rationed care monitoring in Australian residential aged care facilities.

Personalised Travel recommendations and itineraries powered by robust AI. The project's focus on addressing challenges related to input noise, including background noise, accents, and speech recognition errors, reflects a commitment to innovation in the field of AIOT.

Deep learning enabled wearable sensors to facilitate a predictive warning framework aimed at improving the occupational health and safety of steel construction workers.


Telstra-UOW Hub Manager: Tim Davies


Phone: 02 4221 3577