• University of Exeter Featured PhD Programmes
  • University of Surrey Featured PhD Programmes
  • Northumbria University Featured PhD Programmes
  • University of Birmingham Featured PhD Programmes
  • University of Macau Featured PhD Programmes
  • University of Manchester Featured PhD Programmes
  • University of Stirling Featured PhD Programmes
King’s College London Featured PhD Programmes
University Medical Center Utrecht Featured PhD Programmes
Engineering and Physical Sciences Research Council Featured PhD Programmes
Anglia Ruskin University Featured PhD Programmes
FindA University Ltd Featured PhD Programmes

Realising a Data Veracity Framework for the Internet of Things


Project Description

The Internet of Things (IoT) refers to the seamless integration of physical objects, sensors and mobile devices into the information network. The IoT encompasses numerous technologies, services and standards and is seen by many as the cornerstone of the emerging ICT market. The UK Government Office for Science report The Internet of Things: Making the Most of the Second Digital Revolution identifies trust and public acceptability both as central to IoT and as sources of considerable uncertainty.

Data provenance is recognised as one of the key enablers of a trusted information infrastructure – as it exposes the history of data and any subsequent manipulation/modifications. Consider the case of a faulty IoT device that reports incomplete or erroneous data – that has the potential to result in real physical consequences. Only by understanding the context of the device and the data it produces can we (or automated systems) determine if the data are reliable and actionable. While veracity is often listed as one of the seven Vs of Big Data, along with volume, velocity, variety, variability, visualization and value – it is perhaps the most important and least studied.

You will explore the following questions:

How can data veracity measures (metrics) be encoded and enacted within an IoT ecosystem?

How can data provenance be used to support new forms of veracity checking and anomaly detection?

How can data policies be framed to reason about data veracity, and recommend appropriate decision-making actions?

Real-world examples drawn from the EPSRC TrustLens project will be used, including scenarios based on data generated by Internet of Things devices.

The successful candidate will have or expect to have a UK Honours Degree at 2.1 (or equivalent) in COMPUTER SCIENCE or related discipline.

Essential background: Data science; programming in Python or Java.
Knowledge of: Desirable - Semantic Web, knowledge representation and reasoning.

APPLICATION PROCEDURE:
this project is advertised in relation to the research areas of the discipline of Computing Science. Formal applications can be completed online: https://www.abdn.ac.uk/pgap/login.php You should apply for Degree of Doctor of Philosophy in Computing Science, to ensure that your application is passed to the correct person for processing.

Informal inquiries can be made to Professor P Edwards () with a copy of your curriculum vitae and cover letter indicating your interest in the project and why you wish to undertake it. All general enquiries should be directed to the Postgraduate Research School ().

Funding Notes

There is no funding attached to this project, it is for self-funded students only.

Email Now

Insert previous message below for editing? 
You haven’t included a message. Providing a specific message means universities will take your enquiry more seriously and helps them provide the information you need.
Why not add a message here
* required field
Send a copy to me for my own records.

Your enquiry has been emailed successfully




Let us know you agree to cookies

We use cookies to give you the best online experience. By continuing, we'll assume that you're happy to receive all cookies on this website. To read our privacy policy click here

Ok