Summary
IoT devices are being used in a wide range of applications, including healthcare, and smart cities. To perform sensing of their environment, IoT devices are typically deployed as a distributed system. There has recently been increased interest in these devices to perform more complex tasks. One issue is that many of these devices have limited processing power, data storage, power storage, and other constraints. Because of these resource constraints, IoT devices will be unable to perform computationally expensive tasks such as machine learning and will require support from more powerful computing devices. One possibility could be an Edge-based digital twin for the machine learning-based process running on the IoT device, where the computation could be offloaded. While there are numerous potential benefits to this compatible digital twin system, implementing these solutions will necessitate careful planning and consideration of trust establishment and privacy if maximum benefits are to be realised. The computation offloading between the digital twin and its physical counterpart will require trustable, secure, and privacy-conscious communication.
This project aims to accelerate the development of a digital twin by implementing trustable computation offloading. It will extend the life of the IoT network by offloading heavy computations to the network's more powerful devices and will develop novel models to support the development of trustworthy computation offloading based on digital twins. It is an interdisciplinary research project within the School of Computing that combines research expertise and previous research results in IoT trust establishment, machine learning, and pervasive computing.
Essential criteria
Applicants should hold, or expect to obtain, a First or Upper Second Class Honours Degree in a subject relevant to the proposed area of study.
We may also consider applications from those who hold equivalent qualifications, for example, a Lower Second Class Honours Degree plus a Master’s Degree with Distinction.
In exceptional circumstances, the University may consider a portfolio of evidence from applicants who have appropriate professional experience which is equivalent to the learning outcomes of an Honours degree in lieu of academic qualifications.
Desirable Criteria
If the University receives a large number of applicants for the project, the following desirable criteria may be applied to shortlist applicants for interview.
- First Class Honours (1st) Degree
- Masters at 70%
- Experience using research methods or other approaches relevant to the subject domain
- Work experience relevant to the proposed project
- Publications - peer-reviewed
Funding and eligibility
The University offers the following levels of support:
Vice Chancellors Research Studentship (VCRS)
Full award (full-time PhD fees + DfE level of maintenance grant + RTSG for 3 years).
This scholarship will cover full-time PhD tuition fees and provide the recipient with £16,062 (tbc) maintenance grant per annum for three years (subject to satisfactory academic performance). This scholarship also comes with £900 per annum for three years as a research training support grant (RTSG) allocation to help support the PhD researcher.
Applicants who already hold a doctoral degree or who have been registered on a programme of research leading to the award of a doctoral degree on a full-time basis for more than one year (or part-time equivalent) are NOT eligible to apply for an award.
Vice-Chancellor’s Research Bursary (VCRB)
Part award (full-time PhD fees + 50% DfE level of maintenance grant + RTSG for 3 years).
This scholarship will cover full-time PhD tuition fees and provide the recipient with £8,000 maintenance grant per annum for three years (subject to satisfactory academic performance). This scholarship also comes with £900 per annum for three years as a research training support grant (RTSG) allocation to help support the PhD researcher.
Applicants who already hold a doctoral degree or who have been registered on a programme of research leading to the award of a doctoral degree on a full-time basis for more than one year (or part-time equivalent) are NOT eligible to apply for an award.
Vice-Chancellor’s Research Fees Bursary (VCRFB)
Fees only award (PhD fees + RTSG for 3 years).
This scholarship will cover full-time PhD tuition fees for three years (subject to satisfactory academic performance). This scholarship also comes with £900 per annum for three years as a research training support grant (RTSG) allocation to help support the PhD researcher.
Applicants who already hold a doctoral degree or who have been registered on a programme of research leading to the award of a doctoral degree on a full-time basis for more than one year (or part-time equivalent) are NOT eligible to apply for an award.
Department for the Economy (DFE)
The scholarship will cover tuition fees at the Home rate and a maintenance allowance of £16,062 (tbc) per annum for three years (subject to satisfactory academic performance). This scholarship also comes with £900 per annum for three years as a research training support grant (RTSG) allocation to help support the PhD researcher.
- Candidates with pre-settled or settled status under the EU Settlement Scheme, who also satisfy a three year residency requirement in the UK prior to the start of the course for which a Studentship is held MAY receive a Studentship covering fees and maintenance.
- Republic of Ireland (ROI) nationals who satisfy three years’ residency in the UK prior to the start of the course MAY receive a Studentship covering fees and maintenance (ROI nationals don’t need to have pre-settled or settled status under the EU Settlement Scheme to qualify).
- Other non-ROI EU applicants are ‘International’ are not eligible for this source of funding.
- Applicants who already hold a doctoral degree or who have been registered on a programme of research leading to the award of a doctoral degree on a full-time basis for more than one year (or part-time equivalent) are NOT eligible to apply for an award.
Due consideration should be given to financing your studies. Further information on cost of living
Recommended reading
Fagherazzi G, Deep Digital Phenotyping and Digital Twins for Precision Health: Time to Dig Deeper J Med Internet Res 2020;22(3):e16770, doi: 10.2196/16770
Bradbury, M., Jhumka, A., Watson, T., Flores, D., Burton, J. and Butler, M., 2022. Threat-modeling-guided Trust-based Task Offloading for Resource-constrained Internet of Things. ACM Transactions on Sensor Networks (TOSN), 18(2), pp.1-41.
Torous, J., Onnela, JP. & Keshavan, M. New dimensions and new tools to realize the potential of RDoC: digital phenotyping via smartphones and connected devices. Transl Psychiatry 7, e1053 (2017). https://doi.org/10.1038/tp.2017.25
Huckvale, K., Venkatesh, S. & Christensen, H. Toward clinical digital phenotyping: a timely opportunity to consider purpose, quality, and safety. npj Digit. Med. 2, 88 (2019). https://doi.org/10.1038/s41746-019-0166-1
Fallmann S., Chen L. and Chen F., Enhanced Multi-Source Data Analysis for Personalized Sleep-Wake Pattern Recognition and Sleep Parameter Extraction, Personal and Ubiquitous Computing, https://doi.org/10.1007/s00779-020-01445-9 , 2020.
Chen L., Nugent C., Human Activity Recognition and Behaviour Analysis For Cyber-Physical Systems in Smart Environments, DOI: https://doi.org/10.1007/978-3-030-19408-6, Springer, ISBN:978-3-030-19407-9, https://www.springer.com/gp/book/9783030194079, pp255, 2019.
Ning H., Chen L., Ullah A., Luo X., Cyber-enabled Intelligence, CRC Press, Taylor & Francis, ISBN 9780367184872, 2019; https://www.crcpress.com/Cyber-Enabled-Intelligence/Ning-Chen-Ullah-Luo/p/book/9780367184872
Triboan D., Chen L., Chen F., Z. Wang, A Semantics-based Approach to Sensor Data Segmentation in Real-time Activity Recognition, Future Generation Computer Systems, https://doi.org/10.1016/j.future.2018.09.055 , vol.93, pp.224-236, 2019.
