Natural disasters (e.g. tsunamis; earthquakes and floods) and human-made disasters can severely damage the critical infrastructure (a body of networks, systems and assets of a country), which directly affects the country’s economy and security. In such a scenario, the humans might not be first responders due to physical inaccessibility and lack of communication facilities, leading to further loss of life and property. Emergency management (EM) networks can play a very critical role in mitigating the impacts of disasters by providing mobile health services for people in distress and creating a communication link between the affectees and government agencies, especially in remote and difficult to reach areas.
The current platforms lack the resilience and the intelligence to help the people in distress by providing mobile health services effectively and help the government agencies to formulate an informed rescue and rehabilitation plan. The networks are susceptible to blockages in case of node failures and the current solutions lack the automation required for self-healing and self-organising capability for network sustainability. Moreover, human interaction required to set-up and support the connectivity incurs costs and resources, thereby compromising the efficiency of the network. The intelligence in the network is missing with regards to taking decisions according to the contextual information such as propagation environment (urban or rural) and other physical constraints.
Fifth generation (5G) mobile (Wireless) technology and its key enabling technologies such as device-to-device (D2D) and internet-of-things (IoT) can be leveraged to develop an EM network. Moreover, the unmanned aerial vehicles (UAVs) can assist EM networks due to their ease of mobility.
This project will look into developing an intelligent and autonomous EM network, where the network is based on the integration of IoT and UAVs. The aim is to develop an architecture that can realize the autonomous nature of the network through self-healing and self-organising algorithms that cater to node failures with minimal delay and signalling. The self-healing algorithms will be empowered by machine learning (ML) techniques, considering the network contextual information.
The first challenge will be to develop the analytical models that can help in quantifying the efficiency of the proposed architecture in terms of end-to-end success and resource efficiency. The second challenge will be to use a network simulator to validate the analytical models and evaluate the proposed architecture from a more practical perspective. The proposed architecture will be evaluated based on the requirements of 5G networks such as ultra-reliable low-latency communications (URLLC) and optimising the parameters for ensuring smooth mobile-health services and public safety.
The Principal Supervisor for this project is Dr. Rafay Ansari.
Eligibility and How to Apply:
Please note eligibility requirement:
- Academic excellence of the proposed student i.e. 2:1 (or equivalent GPA from non-UK universities [preference for 1st class honours]); or a Masters (preference for Merit or above); or APEL evidence of substantial practitioner achievement.
- Appropriate IELTS score, if required.
- Applicants cannot apply for this funding if currently engaged in Doctoral study at Northumbria or elsewhere or if they have previously been awarded a PhD.
For further details of how to apply, entry requirements and the application form, see
Please note: Applications that do not include a research proposal of approximately 1,000 words (not a copy of the advert), or that do not include the advert reference (e.g. RDF22-R/…) will not be considered.
Deadline for applications: 20 June 2022
Start Date: 1 October 2022
Northumbria University takes pride in, and values, the quality and diversity of our staff and students. We welcome applications from all members of the community.
Funding Notes: Each studentship supports a full stipend, paid for three years at RCUK rates (for 2022/23 full-time study this is £16,602 per year) and full tuition fees. Only UK candidates may apply.
Studentships are available for applicants who wish to study on a part-time basis over 5 years (0.6 FTE, stipend £9,961 per year and full tuition fees) in combination with work or personal responsibilities.
Please note: to be classed as a Home student, candidates must meet the following criteria:
• Be a UK National (meeting residency requirements), or
• have settled status, or
• have pre-settled status (meeting residency requirements), or
• have indefinite leave to remain or enter.