A Natural Computation Approach to Real-time Big Data Challenges in the Internet of Things


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About this project

This PhD project will investigate, design and implement novel natural computation-based algorithms such as: multi-objective optimisers; gene regulatory networks; and deep learning, to process big data from an Internet of Things (IoT) network of smart nodes. The Internet of Things connects physical devices containing some form of embedded electronics, a variety of sensors/actuators, and communications hardware to allow the devices to exchange data. Due to the size and complexity of IoT networks, devices that rapidly gather data from diverse surrounding environments generate very big volumes of streamed data. This cannot be stored due to limited capacity and requires real-time processing and analysis to act effectively and efficiently in a variety of applications, such as: wind farm monitoring and early fault detection, operational efficiency improvement, traffic logistics, smart homes/cities and autonomous vehicles/drone swarm navigation. Algorithms and software will be developed for intelligent, efficient processing and analysis of streamed data from the network in real-time, harnessing the distributed parallel processing power of the nodes, in order to identify trends in the data, highlighting abnormal/unusual behaviour, and instructing smart nodes of the network to adapt or act accordingly.The IoT network could be implemented as either a simulation model, which can utilise the high performance computing resources available at the University, and/or using a wirelessly connected network of embedded devices and sensors.


Lead Supervisor: Dr James Walker,


Prof Ken Hawick

Dr David Chalupa

Next steps


Full-time UK/EU PhD Scholarships will include fees at the ‘home/EU' student rate and maintenance (£14,121 in 2016/17) for three years, depending on satisfactory progress.

Full-time International Fee PhD Studentships will include full fees at the International student rate for three years, dependent on satisfactory progress.

Entry requirements

Candidates should have excellent programming skills and a degree in Computer Science or a related discipline. Experience in machine learning is essential. Further knowledge and expertise of one or more of the following areas is highly desirable: evolutionary algorithms, deep learning, natural language processing, embedded systems and the Internet of Things (IoT).

Successful applicants will be informed of the award as soon as possible and by 8th May 2017 at the latest.


Find out more about research in Computer Science.

How to apply

Application deadline: 13 March 2017

Research options:

PhD: 4 years (full-time)

The Postgraduate Training Scheme

PhD students at the University of Hull follow modules for research and transferable skills development and gain a Masters level Certificate, or Diploma, in Research Training, in addition to their research degree.