18 July 2022
About the research project
The Industrial Internet of Things (IIoT) gathers a large amount of data for a specific target environment. The data can describe the environment's state and is used for future decisions regarding the system. Blockchain gives the assurance that the data used or generated by the IoT devices is valid and reliable. When combined with blockchain, IoT devices enable the complete automation of different tasks that do not require constant human interactions or monitoring. Typical examples of IoT and Blockchain in the industries include automatic payments and supply chain management.
IIoT can replace manual maintenance and machine-related diagnostics with a slow but continuous online process to predict and prevent future failures. A key challenge with implementing IIoT is determining the correct configuration that enables the system to capture the best possible data set for decision making. Various reasons for failure include broken individual devices, damaged network setup or incorrect deployment strategies. Blockchain can provide integrity to this approach of self-monitoring and diagnosis of IIoT to provide an appropriate level of service quality by deploying the monitoring scheme in a secure, trusted and distributed manner.
The research aim of this project will be to determine the proper data storage methods and possible data moderation between different sources of data from multiple IIoT devices. Devices placed on prototype machinery will be used to collect data for creating a health index for the machines and the devices connected to them. The outcome will be a method to properly differentiate and isolate the failures of the IIoT devices to capture the machine's states correctly, from the actual failures of the machine and quantify this difference using machine learning techniques. It will further develop strategies to assimilate newly identified persistent problems into an existing pool of know issues about a particular IIoT system.
Primary SupervisorMeet Dr Ananda Maiti
Applicants will be considered for a Research Training Program (RTP) scholarship or Tasmania Graduate Research Scholarship (TGRS) which, if successful, provides:
- a living allowance stipend of $28,854 per annum (2022 rate, indexed annually) for 2 years (MRes) or 3.5 years (PhD)
- a relocation allowance of up to $2,000
- a tuition fees offset covering the cost of tuition fees for up to 2 years (MRes) or 4 years (PhD) (domestic applicants only)
If successful, international applicants will receive a University of Tasmania Fees Offset for up to 2 years (MRes) or 4 years (PhD)
As part of the application process you may indicate if you do not wish to be considered for scholarship funding.
Applicants should review the Higher Degree by Research minimum entry requirements.
Additional eligibility criteria specific to this project/scholarship:
- Applicants must be able to undertake the project on-campus
The project is competitively assessed and awarded. Selection is based on academic merit and suitability to the project as determined by the College.
Additional essential selection criteria specific to this project:
- Applicants are expected to have an understanding of IoT systems and related data management. They should have technical skills to implement basic prototype IoT devices
Additional desirable selection criteria specific to this project:
- Experience in Machine learning techniques for anomaly/fault detections
There is a three-step application process:
- Select your project, and check you meet the eligibility and selection criteria;
- Contact the Primary Supervisor, Dr Ananda Maiti to discuss your suitability and the project's requirements; and
- Submit an application by the closing date listed above.
- Copy and paste the title of the project from this advertisement into your application. If you don’t correctly do this your application may be rejected.
- As part of your application, you will be required to submit a covering letter, a CV including 2 x referees and your project research proposal.
Following the application closing date applications will be assessed within the College. Applicants should expect to receive notification of the outcome by email by the advertised outcome date.