An AI-driven approach to proactive Internet Of Things (IoT) based systems

   School of Computing, Engineering & the Built Environment

   Applications accepted all year round  Self-Funded PhD Students Only

About the Project

The Internet of Things (IoT) refers to the ever-growing network of physical objects such as smart devices, vehicles, buildings and other items embedded with electronics, software, sensors, actuators, and network connectivity that enable these objects to interact autonomously and intelligently. IoT is the foundation of so-called smart systems which provide critical services in many emerging application domains such as smart home, smart city, energy supply and traffic management. IoT based smart systems is creating huge new markets and solutions to improve our economy, society and life. They have been a key drive in many economic sectors and many parts of the society and people’s life.

However, when use the actual smart systems in practice, people often feel that the system is not that smart as they expected or as advocated by the vendors. Disappointingly, the systems often produce very limited or even wrong response to the user need. The current research on building the smart systems is still quite basic and leaving the current smart systems with no or little learning abilities to understand the user need at an appropriate deep level. This problem becomes worse because user needs are usually dynamic, i.e. changing from time to time. Furthermore, the advances of sensors and computing technologies plus the wide spectrum of application domains have made these smart systems very diverse, large and complex.

In this PhD project, the successful candidate will explore the current state of the art on software architecture and Internet Of Things and then develop a new approach to endorsing the proactive learning ability to the IoT and therefore enable these smart systems to provide resilient and adaptive services that best match the dynamically changing user needs. The approach will provide a key solution to one of the greatest concerns of the current IoT-based smart systems.

Applications from potential part-time students are also welcomed. 

Academic qualifications

A first degree (at least a 2.1) ideally in Computer Science with a good fundamental knowledge of software engineering, or artificial intelligence or Internet Of Things.

English language requirement

IELTS score must be at least 6.5 (with not less than 6.0 in each of the four components). Other, equivalent qualifications will be accepted. Full details of the University's policy are available online.

Essential attributes

  • Experience of fundamental software design and development
  • Competent in design of Internet Of Things applications
  • Knowledge of software architecture
  • Good written and oral communication skills
  • Strong motivation, with evidence of independent research skills relevant to the project
  • Good time management

Desirable attributes

  • Some knowledge of machine learning would be beneficial
Computer Science (8)

Funding Notes

This is an unfunded position


“Context-Active Resilience in Cyber Physical Systems (CAR)”, EU H2020 Marie Skłodowska-Curie Actions – European Fellowships Project, Coordinator, 2016-2018, .
Yang, Z., Wu, H., Liu, Q., Liu, X., Zhang, Y., & Cao, X. A self-attention integrated spatiotemporal LSTM approach to edge-radar echo extrapolation in the Internet of Radars. ISA Transactions, Elsevier, Vol 132, 2022.
Liu, Q., Kamoto, K. M., Liu, X., Zhang, Y., Yang, Z., Khosravi, M. R., Qi, L. A Sensory Similarities Approach to Load Disaggregation of Charging Stations in Internet of Electric Vehicles. IEEE Sensors Journal, 21(14),, 2020.
Qi Liu, Kondwani Michael Kamoto, Xiaodong Liu, Mingxu Sun, Nigel Linge. Low-Complexity Non-Intrusive Load Monitoring Using Unsupervised Learning and Generalized Appliance Models. IEEE Transactions on Consumer Electronics, 65(1), 1-1, 2019.
Daren Fang, Xiaodong Liu, Imed Romdhani and Claus Pahl, An agility-oriented and fuzziness-embedded semantic model for collaborative cloud service search, retrieval and recommendation. Future Generation Computer Systems, Elsevier, Vol. 56, Issue C, pp 11-26, 2016.

Register your interest for this project