This project has two different research themes: (1) Edge Computing; (2) Data Science
(1) Edge Computing is the notion of performing computation close to the data generation source, by significantly reducing data communication latency and ensuring that private data is maintained closer to the data generation source. Ideal edge computing infrastructures should be able to dynamically configure and adapt itself to facilitate a given sensing and security requirement, in the most efficient way. Edge computing is a novel research area, and often seen as the future of cloud computing for supporting real-time processing.
This theme design and evaluate novel edge resource orchestration algorithms, to address research challenges such as: i. How to orchestrate service execution across different edge resources based on a number of constraints (e.g. energy, cost, ownership, privacy) ii. How to dynamically adapt edge resources to meet Quality of Service or Quality of Experience targets for specific applications iii. Use of edge computing resources for particular application scenarios – e.g. emergency response, “smart” buildings, elderly care, smart factory, etc.
We have a 100 node Edge computing infrastructure developed using RaspberryPis to conduct the evaluations.
(2) Data Science (Including Interdisciplinary Research): Understanding how data from edge resources can be analysed to address particular research questions in a timely and efficient manner, such as: i. Trading comfort for energy efficiency in built environments; ii. Use edge resources in autonomous vehicles; iii. Wellbeing and Healthcare in homes and workspaces