University of Edinburgh Featured PhD Programmes
University of Leeds Featured PhD Programmes
University of Exeter Featured PhD Programmes

Distributed artificial intelligence for Internet of things (Application Ref: SF19/EE/CIS/JIN)

Faculty of Engineering and Environment

This project is no longer listed on and may not be available.

Click here to search for PhD studentship opportunities
Dr N Jin , Dr X Dai Applications accepted all year round Self-Funded PhD Students Only

About the Project

Internet of things (IoT) has been transforming many industrial sectors and our daily life, for example digital manufacturing, smart grid and digital living.
IoT systems have generated an increasingly large volume of data at network edge. Currently AI based data analytic solutions often collect all data in an IoT system, then to discover knowledge at a data centre, and to provide centralized decision support. This suffers from high latency, cost inefficiency, and is constrained by network capacity and traffic.
This research project will address this challenge by analysing data at end point devices, typically at the smart sensors, and to distribute intelligence among devices in neighbourhood, gateways and the data centre (often the cloud). The output of this project will deliver a theoretic framework, implementation of prototypes, and demonstrable test scenarios in digital manufacturing.
In details, we will investigate whether to discover knowledge at end devices (edge), gateways, and then to distribute knowledge across IoT layers will significantly improve responsiveness and reduce data volume in communication. We will propose trade-off algorithms which dynamically optimize local data analysis and centralized aggregation and decision making, under constraints of various hardware specifications and network situations.
It is an interdisciplinary research project. The supervision team has one expert in AI, and another expert in IoT. Moreover, this project will collaborate with a local IoT company which we have already established a long-term partnership

This project is supervised by Dr Nanlin Jin.

Eligibility and How to Apply:

Please note eligibility requirement:
• Academic excellence of the proposed student i.e. 2:1 (or equivalent GPA from non-UK universities [preference for 1st class honours]); or a Masters (preference for Merit or above); or APEL evidence of substantial practitioner achievement.
• Appropriate IELTS score, if required.

For further details of how to apply, entry requirements and the application form, see

Please note: Applications that do not include a research proposal of approximately 1,000 words (not a copy of the advert), or that do not include the advert reference (e.g. SF19/EE/CIS/JIN) will not be considered.

Start Date: 1 March 2020 or 1 October 2020

Northumbria University takes pride in, and values, the quality and diversity of our staff. We welcome applications from all members of the community. The University holds an Athena SWAN Bronze award in recognition of our commitment to improving employment practices for the advancement of gender equality and is a member of the Euraxess network, which delivers information and support to professional researchers.

Funding Notes

This is an unfunded research project.


Tom Wilcox, Nanlin Jin, Peter Flach, Joshua Thumim. “A Big Data platform for smart meter data analytics". Vol 105, Computers in Industry, Elsevier, 2019. Impact Factor: 4.769.

Ranti Endeley, Tom Fleming, Nanlin Jin, Gerhard Fehringer and Steve Cammish. “A Smart Gateway enabling OPC UA and DDS interoperability for IoT". IEEE Smart World Congress, IEEE, 2019.

Jeremy Ellman, Nathan Lee, Nanlin Jin. “Cloud Computing Deployment: A Cost-Modelling
Case-Study", Wireless Networks, Springer, 2018. Impact Factor: 2.405

Anthony Bagley, Gerhard Fehringer, Nanlin Jin, Steve Cammish, “Live Video Transmission over Data Distribution Service with Existing Low-Power Platforms". International Conference on Internet of Things Data, and Cloud Computing, ACM, 2017

Reem Al-Otaibi, Nanlin Jin, Tom Wilcox, Peter Flach, “Feature Construction and Calibration for Clustering Daily Load Curves from Smart Meter Data”. Vol 12 issue 2, IEEE Trans. Industrial Informatics, 2016. Impact factor: 7.377.

Nanlin Jin, Peter Flach, Tom Wilcox, Royston Sellman, Joshua Thumim, Arno J. Knobbe.
“Subgroup Discovery in Smart Electricity Meter Data" IEEE Trans. Industrial Informatics
10(2): 1327-1336, 2014. Impact factor: 7.377.

FindAPhD. Copyright 2005-2021
All rights reserved.