Project description: Disasters are events that can occur at any time and can cause a severe loss of life and damage existing infrastructure. In these events, the victim needs to communicate, and first responders need to coordinate their efforts to provide an efficient and timely response, which may include data, voice, and video transmission. The existing wireless technology has been proposed widely to be used in case of emergencies, like GSM, LTE, Satellite, Internet-of-Things, etc. To be useful, these technologies must fulfil the requirements for an emergency response network, which includes, rapid deployment, interoperability, quality of service, coverage, mobility, agility, and self-organization. Most importantly, autonomous communication models are required for these situations to avoid the need for manual configuration and management. For example, LTE-based mobile small cells can be used in case an existing base station is damaged. However, the antenna direction and alignment, the placement of the cells, frequency usage planning, congestion avoidance, and resource management are some of the challenges, which need to be addressed. To address these challenges for an emergency network, autonomous network models are required to support the self-organization, configuration, and management of different network operations.
- To propose autonomous network models to facilitate rapid deployment of mobile base stations in a post-disaster situation, while addressing some challenges like spectrum management/planning, placement/localization of mobile robots, radio resource management, front-haul and back-haul links establishment.
- To propose autonomous cross-layer protocol design/models (physical, MAC, and network) to facilitate data transmission, support network operations, with congestion/traffic control while maintaining and providing the desired network performance.
- The models and protocols will be implemented on the NS3 network simulator, and feasibility will be shown using prototypes.
We are looking for an excellent, motivated, and self-driven student to engage in Ph.D. research. The desired candidate should essentially have a master’s degree in Computer Science/Engineering. Bachelor’s degree holders can apply provided they show strong motivation and proficiency. The ideal candidate should have strong mathematical modeling and machine learning skills. Strong programming skills especially in C++ and Python are required. The candidate must have English proficiency and good communication skills. Candidates with related publications will be given preference.
please send to Richella Murphy, [Email Address Removed] only using the application form.
Application Form / Terms of Conditions can be obtained on the website:
The closing date for receipt of applications is 5pm, (GMT) 121st February 2022