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Accelerating sustainability using digital twin and blockchain for business decision modelling

   School of Electronics, Electrical Engineering and Computer Science

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  Dr Vishal Sharma, Prof Karen Rafferty  No more applications being accepted  Funded PhD Project (Students Worldwide)

About the Project

Project Introduction:

This proposed research aligns to the Advanced Research and Engineering centre within Northern Ireland which brings together expertise from PwC, University of Ulster and Queen’s University Belfast.  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, bringing the total stipend to approximately £21,601 per annum.   

 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. 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. 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.

Project Description:

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 that 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. 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. 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 their business practice.

Project Key Words:

Digital Twin, Blockchain, Sustainability, FinTech, Decision-Making, Automation, Security


Start Date: 01/10/22

Application Closing date: 28/02/22

Funding Body: DfE / PwC

Project Funding Type: funded

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

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 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:


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.
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.
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.
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