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PhD Opportunities

Accelerating sustainability using digital twin and blockchain for business decision modelling

School of Electronics, Electrical Engineering and Computer Science | PHD

Applications are now CLOSED
Funding
Funded
Reference Number
EEECS/2022/PWC1
Application Deadline
28 February 2022
Start Date
1 October 2022

Overview

This proposed research aligns to The new Advanced Research and Engineering centre within Northern Ireland. This Centre will drive future innovations in technology and enhance our capabilities in important research areas such as robotic process automation (RPA), workflow automation, visualisation, data analytics and artificial intelligence (AI). The Centre brings together expertise from PwC, University of Ulster and Queen’s University Belfast. This research project aligns to the workflow and AI streams within the Centre. A selection process will determine the strongest candidates across a range of projects, who may then be offered funding for their chosen project. Approximately £6000 per year is payable to the sponsored student in addition to the normal stipend. Modern businesses aim to improve the coordination between the digital world with precise coordination with human-centric computing. Having sustainable, reliable, coordinated and resilient activities will be critical to any business. Such requirements can be empowered using Digital Twin (DT) and blockchain. DT enables a complete physical to virtual mapping, allowing businesses to be ahead in decision-making by leveraging the virtual integrations of devices, AI, and risk elements. Automated data exchanges, live feed from platforms, and users' inputs facilitate the operations of DTs. It leads to a one-in-all platform to assist in tasks ranging from regular activities such as device monitoring to complex decision-making that requires analysis of the deployment state's functions that include high cost or even the risk of life. Knowing the impact of actions in a business when the products are operating is a tremendous advantage of DTs. With a large amount of data available from real-world products, analytics, estimation, and predictions approaches can be applied by design to facilitate the decision-making of DTs. Overall, the business advantages would be reduced risks, improved decision efficiency, reduced cost via better analysis of CAPEX\OPEX with reduced efforts of person-months on maintenance, near-world estimation and extensive visualization – all leading to high sustainability. Despite the advantages, certain aspects need to be carefully investigated and require models on top of DTs to assist in operations. Some of the constraints are accurate and precise mapping, trust-between entities and data exchange, along with security. Here, security must not impact the efficiency, and an equilibrium must be maintained when used. The challenge with data security is critical as a large amount of real-time and historical data gets generated from this virtual-physical world mappings. It further includes identifying security concerns from the data and ensuring that attacks do not compromise DT's operations, information life, and device health. Here, blockchain is a promising direction to consider for securing DTs and simultaneously facilitating the DT-supply chain within an organization. Blockchain can offer a safe, reliable and decentralized mechanism of maintaining operations related to devices, lifecycle, and data, and more prominently without compromising the security of the business units. Including blockchain within the DT will offer a considerable advantage of service interoperability where several business units can be combined within an organization, scaling the benefits of DTs. This state-of-the-art research will be pivotal in exploring and developing solutions to include DTs with blockchain for digitalizing businesses.

Utilizing DT in businesses aiming towards digitalization is not a simple mechanism; it involves several complex operations between the physical and virtual world coupled together, which also needs to ensure that processes and data security remain intact. This project aims to deliver a sustainable solution for using DT and blockchain to digitalize businesses, focusing on reducing risks, improved decision efficiency, reduced cost, and better operational activities. The project will investigate the business processes included in the DT and explore the dependencies around data and between other business functions. The project will leverage data analytics, prediction and estimation technologies supported by the blockchain to provide a complete solution for automating and digitalizing decision making.

The project initially involves building a permissioned-blockchain setup that can be operational with high-frequency data from several devices within the business. The blockchain will be operated via a trust mechanism to ensure process supply chain management for the continuous and secure working of the DTs. Blockchain will form the underlying data layer for the DTs, which other processes can query in the DT to perform local predictions and estimations. It will be a novel direction as there are not many evident blockchain solutions that are deployable for many devices without impacting the performance and consuming less computational resources. The data entanglements are manageable using distributed data analysis, and associated cascading failures are predictable by using AI models from the data stored on the blockchain without interfering with the operations of the DTs.

Next, the project will develop several models, including rigorous formal validation, to understand the dependencies of the processes and the number of interactions allowed with the real-world products. It will consider the privileges on the data and the level of risks assessment associated with the data. Once the models are built and validated, the project will move to the development of the DT platform, which will have several layers of visualization. We will also develop strategies to assess the risk and test scenarios in real-time without affecting the actual product or remodelling the DT. Here, we will be developing models of automated decision making by exploring directions of Markov Decision Processes, etc, which helps to make accurate strategies on the DTs.

The next task will be to connect the blockchain and DT to complete the data flows, allow tracing of the processes, and manage the logs. Few additional features will be researched and developed, such as operating even if there is a partial data loss or equipment failure. Here, machine learning models will be explored on top of the DT to comprehensively support decision making, enhance the understanding of the data via visualizations, and report and reduce associated errors.

Other tasks will involve a series of simulated application case studies followed by practical testing in different phases of this project. The expectations are that the combination of intelligent decision making via DT facilitated by blockchain and formal modelling will enable businesses to further explore and use this research and prototypes to further enhance the engineering aspects or identify additional directions to explore and develop.

Selected Readings:

[1] Wang FY, Qin R, Li J, Yuan Y, Wang X. Parallel societies: A computing perspective of social digital twins and virtual–real interactions. IEEE Transactions on Computational Social Systems. 2020 Feb 21;7(1):2-7.

[2] Khan A, Shahid F, Maple C, Ahmad A, Jeon G. Towards Smart Manufacturing Using Spiral Digital Twin Framework and Twinchain. IEEE Transactions on Industrial Informatics. 2020 Dec 29; 18(2): 1359-1366.

[3] Sharma V, Szalachowski P, Zhou J. Evaluating Blockchain Protocols with Abusive Modeling. In Proceedings of 17th ACM ASIA Conference on Computer and Communications Security (ACM ASIACCS 2022), 30/05/2022 to 03/06/2022 Nagasaki, Japan.

[4] Lu Y, Huang X, Zhang K, Maharjan S, Zhang Y. Communication-efficient federated learning and permissioned blockchain for digital twin edge networks. IEEE Internet of Things Journal. 2020 Aug 11;8(4):2276-88.

[5] Nguyen T, Duong QH, Van Nguyen T, Zhu Y, Zhou L. Knowledge mapping of digital twin and physical internet in Supply Chain Management: A systematic literature review. International Journal of Production Economics. 2022 Feb 1; 244:108381.

Funding Information

This three year studentship, for full-time PhD study, is potentially funded by the Department for the Economy (DfE) and commences on 1 October 2022. For UK domiciled students the value of an award includes the cost of approved tuition fees as well as maintenance support (Fees £4,500 pa and Stipend rate £15,609 pa - 2022-23 rates to be confirmed). To be considered eligible for a full DfE studentship award you must have been ordinarily resident in the United Kingdom for the full three year period before the first day of the first academic year of the course. The candidate must be ordinarily resident in Northern Ireland on the first day of the first academic year of the course, normally 1 October. For further information about eligibility criteria please refer to the DfE Postgraduate Studentship Terms and Conditions 2021-22 at https://go.qub.ac.uk/dfeterms

A selection process will determine the strongest candidates across a range of projects, who may then be offered funding for their chosen project. This is an industrially sponsored project. Approximately £6000 per year is payable to the sponsored student in addition to the stipend rate detailed above. Bringing the total stipend to approximately £21,609 per annum.

For candidates who do not meet the above residency requirements, a small number of international studentships may be available from the School. These are expected to be highly competitive, and a selection process will determine the strongest candidates across a range of School projects, who may then be offered funding for their chosen project.

Academic Requirements:
A minimum 2.1 honours degree or equivalent in Computer Science, Electrical and Electronic Engineering, or Psychology or relevant degree with relevant technological experience.

Applicants should apply electronically through the Queen’s online application portal at: https://dap.qub.ac.uk/portal/

Project Summary
Supervisor
Dr Vishal Sharma
Mode of Study

Full-time: Full Time


Funding Body
DfE / PwC
Apply now Register your interest

Computer Science overview

The School of Electronics, Electrical Engineering and Computer Science (EEECS) aims to enhance the way we use technology in communication, data science, computing systems, cyber security, power electronics, intelligent control, and many related areas.

You’ll be part of a dynamic doctoral research environment and will study alongside students from over 40 countries world wide; we supervise students undertaking research in key areas of computer science, including: computing systems, artificial intelligence and cybersecurity. As part of a lively community of over 100 full-time and part-time research students you’ll have the opportunity to develop your research potential in a vibrant research community that prioritises the cross-fertilisation of ideas and innovation in the advancement of knowledge.

Many PhD studentships attract scholarships and top-up supplements. PhD programmes provide our students with the opportunity to acquire an extensive training in research techniques.

Within the School we have a number of specialist research centres including a Global Research Institute, the Institute of Electronics, Communications and Information Technology (ECIT) specialising in Cyber Security, Wireless Innovation and Data Science and scalable computing.

Computer Science Highlights
Industry Links
  • Queen’s researchers have strong links with the local industry, which boasts a rich mix of local startups and multi-nationals. Belfast is the second fastest growing region in the UK in terms of Knowledge Economy activity (Northern Ireland Economy Report, 2018).
World Class Facilities
  • The state-of-the-art £14m Computer Science Building and the Institute of Electronics, Communications and Information Technology offer bespoke research environments.
Internationally Renowned Experts
  • You will be working under the supervision of leading international academic experts.
Key Facts

Research students are encouraged to play a full and active role in relation to the wide range of research activities undertaken within the School and there are many resources available including:

  • A wide range of personal development and specialist training courses offered through the Personal Development Programme
  • Access to the Queen's University Postgraduate Researcher Development Programme
  • Office accommodation with access to computing facilities and support to attend conferences for full-time PhD students

Course content

Research Information

Associated Research
Research within the School is organised into research themes combining strengths by working together on major projects, in many cases in collaboration with key technology companies.
ECIT brings together internationally recognised research groups specialising in key areas of advanced digital and communications technology.

PhD Opportunities
PhD Opportunities are available in a wide range of computer science subjects, aligned to the specific expertise of our PhD supervisors.

Research Impact
Queen’s is a leader in commercial impact and one of the five highest performing universities in the UK for intellectual property commercialisation. We have created over 80 spin-out companies. Three of these -
Kainos, Andor Technology and Fusion Antibodies - have been publicly listed on the London Stock Exchange.

Research Projects
Queen’s has strong collaborative links with industry in Northern Ireland, and internationally. It has a strong funding track record with EPSRC and the EC H2020 programme.

Research Success
The research profile produced by the 2014 UK Research Excellence Framework (REF) graded 80 per cent of our research activity as 'world-leading' or 'internationally excellent', confirming the School's reputation as an internationally-leading department.

Career Prospects

Introduction
For further information on career opportunities at PhD level please contact the Faculty of Engineering and Physical Sciences Student Recruitment Team on askEPS@qub.ac.uk. Our advisors - in consultation with the School - will be happy to provide further information on your research area, possible career prospects and your research application.

People teaching you




Learning Outcomes

Course structure

There is no specific course content as such. You are expected to take research training modules that are supported by the School which focus on quantitative and qualitative research methods. You are also expected to carry out your research under the guidance of your supervisor.

Over the course of study you can attend postgraduate skills training organised by the Graduate School.

You will normally register, in the first instance, as an ‘undifferentiated PhD student’ which means that you have satisfied staff that you are capable of undertaking a research degree. The decision as to whether you should undertake a PhD is delayed until you have completed ‘differentiation’.

Differentiation takes place about 8-9 months after registration for full time students and about 16-18 months for part time students: You are normally asked to submit work to a panel of up two academics and this is followed up with a formal meeting with the ‘Differentiation Panel’. The Panel then make a judgement about your capacity to continue with your study. Sometimes students are advised to revise their research objectives or to consider submitting their work for an MPhil qualification rather than a doctoral qualification.

To complete with a doctoral qualification you will be required to submit a thesis of approx 80,000 words and you will be required to attend a viva voce [oral examination] with an external and internal examiner to defend your thesis.

A PhD programme runs for 3-4 years full-time or 6-8 years part-time. Students can apply for a writing up year should it be required.

The PhD is open to both full and part time candidates and is often a useful preparation for a career within academia or consultancy.

Full time students are often attracted to research degree programmes because they offer an opportunity to pursue in some depth an area of academic interest.

The part time research degree is an exciting option for professionals already working in the education field who are seeking to extend their knowledge on an issue of professional interest. Often part time candidates choose to research an area that is related to their professional responsibilities.

If you meet the Entry Requirements, the next step is to check whether we can supervise research in your chosen area. We only take students to whom we can offer expert research supervision from one of our academic staff. Therefore, your research question needs to engage with the research interests of one of our staff.

Assessment

- Assessment processes for the Research Degree differ from taught degrees. Students will be expected to present write up their work at regular intervals to their supervisor who will provide written and oral feedback; a formal assessment process takes place annually.

This Annual Progress Review requires students to present their work in writing and orally to a panel of academics from within the School. Successful completion of this process will allow students to register for the next academic year.

The final assessment of the doctoral degree is both oral and written. Students will submit their thesis to an internal and external examining team who will review the written thesis before inviting the student to orally defend their work at a Viva Voce.

Feedback

- Supervisors will offer feedback on the research work at regular intervals throughout the period of registration on the degree.

Facilities

Full time PhD students will have access to a shared office space and access to a desk with personal computer and internet access.

Entrance requirements

Graduate
The minimum academic requirement for admission to a research degree programme is normally an Upper Second Class Honours degree from a UK or ROI HE provider, or an equivalent qualification acceptable to the University. Further information can be obtained by contacting the School.

International Students

For information on international qualification equivalents, please check the specific information for your country.

English Language Requirements

Evidence of an IELTS* score of 6.0, with not less than 5.5 in any component or equivalent qualification acceptable to the University is required (*taken within the last 2 years).

International students wishing to apply to Queen's University Belfast (and for whom English is not their first language), must be able to demonstrate their proficiency in English in order to benefit fully from their course of study or research. Non-EEA nationals must also satisfy UK Visas and Immigration (UKVI) immigration requirements for English language for visa purposes.

For more information on English Language requirements for EEA and non-EEA nationals see: www.qub.ac.uk/EnglishLanguageReqs.

If you need to improve your English language skills before you enter this degree programme, INTO Queen's University Belfast offers a range of English language courses. These intensive and flexible courses are designed to improve your English ability for admission to this degree.

Tuition Fees

Northern Ireland (NI) 1 £4,596
Republic of Ireland (ROI) 2 £4,596
England, Scotland or Wales (GB) 1 £4,596
EU Other 3 £22,700
International £22,700

1 EU citizens in the EU Settlement Scheme, with settled or pre-settled status, are expected to be charged the NI or GB tuition fee based on where they are ordinarily resident, however this is provisional and subject to the publication of the Northern Ireland Assembly Student Fees Regulations. Students who are ROI nationals resident in GB are expected to be charged the GB fee, however this is provisional and subject to the publication of the Northern Ireland Assembly student fees Regulations.

2 It is expected that EU students who are ROI nationals resident in ROI will be eligible for NI tuition fees, in line with the Common Travel Agreement arrangements. The tuition fee set out above is provisional and subject to the publication of the Northern Ireland Assembly student fees Regulations.

3 EU Other students (excludes Republic of Ireland nationals living in GB, NI or ROI) are charged tuition fees in line with international fees.

All tuition fees quoted are for the academic year 2021-22, and relate to a single year of study unless stated otherwise. Tuition fees will be subject to an annual inflationary increase, unless explicitly stated otherwise.

More information on postgraduate tuition fees.

Computer Science costs

There are no specific additional course costs associated with this programme.

Additional course costs

All Students

Depending on the programme of study, there may also be other extra costs which are not covered by tuition fees, which students will need to consider when planning their studies . Students can borrow books and access online learning resources from any Queen's library. If students wish to purchase recommended texts, rather than borrow them from the University Library, prices per text can range from £30 to £100. Students should also budget between £30 to £100 per year for photocopying, memory sticks and printing charges. Students may wish to consider purchasing an electronic device; costs will vary depending on the specification of the model chosen. There are also additional charges for graduation ceremonies, and library fines. In undertaking a research project students may incur costs associated with transport and/or materials, and there will also be additional costs for printing and binding the thesis. There may also be individually tailored research project expenses and students should consult directly with the School for further information.

Bench fees

Some research programmes incur an additional annual charge on top of the tuition fees, often referred to as a bench fee. Bench fees are charged when a programme (or a specific project) incurs extra costs such as those involved with specialist laboratory or field work. If you are required to pay bench fees they will be detailed on your offer letter. If you have any questions about Bench Fees these should be raised with your School at the application stage. Please note that, if you are being funded you will need to ensure your sponsor is aware of and has agreed to fund these additional costs before accepting your place.

How do I fund my study?

1.PhD Opportunities

Find PhD opportunities and funded studentships by subject area.

2.Funded Doctoral Training Programmes

We offer numerous opportunities for funded doctoral study in a world-class research environment. Our centres and partnerships, aim to seek out and nurture outstanding postgraduate research students, and provide targeted training and skills development.

3.PhD loans

The Government offers doctoral loans of up to £26,445 for PhDs and equivalent postgraduate research programmes for English- or Welsh-resident UK and EU students.

4.International Scholarships

Information on Postgraduate Research scholarships for international students.

Funding and Scholarships

The Funding & Scholarship Finder helps prospective and current students find funding to help cover costs towards a whole range of study related expenses.

How to Apply

Apply using our online Postgraduate Applications Portal and follow the step-by-step instructions on how to apply.

Find a supervisor

If you're interested in a particular project, we suggest you contact the relevant academic before you apply, to introduce yourself and ask questions.

To find a potential supervisor aligned with your area of interest, or if you are unsure of who to contact, look through the staff profiles linked here.

You might be asked to provide a short outline of your proposal to help us identify potential supervisors.