About the Project
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.
Edinburgh Napier University is committed to promoting equality and diversity in our staff and student community https://www.napier.ac.uk/about-us/university-governance/equality-and-diversity-information.
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 here https://www.napier.ac.uk/research-and-innovation/research-degrees/application-process
• Experience of fundamental software design and development
• Knowledge in design of Internet Of Things applications
• Good written and oral communication skills
• Strong motivation, with evidence of independent research skills relevant to the project
• Good time management
Some knowledge of machine learning would be beneficial.
Liu, Q., Kamoto, K. M., Liu, X., Sun, M., & Linge, N. (2019). Low-Complexity Non-Intrusive Load Monitoring Using Unsupervised Learning and Generalized Appliance Models. IEEE Transactions on Consumer Electronics, 65(1), 1-1.
Claus Pahl, Frank Fowley, Pooyan Jamshidi, Daren Fang and Xiaodong Liu, “A classification and comparison framework for cloud service brokerage architectures”, IEEE Transactions on Cloud Computing, accepted, online early access first, DOI: 10.1109/TCC.2016.2537333, 2016.
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