Reference number: SCEBE/21SF/021/SN
Aim and Scope
Asset monitoring and tracking in a large healthcare facility is of paramount importance for accurate and real-time information so that the availability of the critical assets can be ensured at the right time and right place to support healthcare delivery. The asset can be monitored through smart sensors such as RFID, Bluetooth Low Energy (BLE), low-resolution imaging and Internet of Things (IoT), and tracked as they move through the infrastructure. However, each of these technologies has their own pros and cons for location accuracy, range and security. An unambiguous and fail-safe solution can utilise blockchain technologies for data security and edge analytics for the management of assets.
Edge-cloud platforms provide a scalable environment for developing end-to-end IoT applications that can collect the information in real time from many IoT devices, aggregate, and analyse the information to manage asset in real-time based on off-loading strategies. To ensure the criticality and timeliness of information, the data will be aggregated and processed closer to the edge end, that is, edge computing and more deliberate processing and data visualisation will take place on the cloud end. Federated learning will be utilised to build upon edge intelligence whilst minimising privacy risks associated with excessive data centralisation. This will ensure asset availability and responsiveness for patient care, and streamline administrative tasks such as end-of-life equipment disposal, calibration, and scheduled maintenance.
This project will investigate algorithms for an unambiguous asset management within a healthcare environment. The data will be co-ordinated in edge-cloud infrastructure for providing real time information and analysis ensuring security through blockchain technologies. The major objectives of the PhD project are as follows:
(i) Development of algorithms for secure collection and analysis of information for asset management in large healthcare facilities;
(ii) Designing a blockchain-based system for ensuring security, sharing and traceability of information within the infrastructure, during transit and on cloud;
(iii) Investigating use of federated learning algorithms to provide edge intelligence into predictive maintenance problems before they occur based on given constraints
To apply for this project, please use the relevant link below
· As a full-time student: https://evision.prod.gcu.tribalsits.com/urd/sits.urd/run/siw_ipp_lgn.login?process=siw_ipp_app&code1=D27CMPTGXFT&code2=0006
· As a part-time student: https://evision.prod.gcu.tribalsits.com/urd/sits.urd/run/siw_ipp_lgn.login?process=siw_ipp_app&code1=D27CMPTGXPT&code2=0006