Objective
5G/6G technology promises to make communication between mobile units more scalable and efficient, and one of the scenarios that has been proposed it the communication between cars based on Mobile Edge Computing.
This thesis will investigate how to this scenario should be designed to save traffic and energy.
Project Description
This thesis aims to investigate realistic deployment patterns for mobile nodes, such as people and vehicles, within edge computing environments, with a focus on optimizing power usage and providing real-time traffic support. As mobile edge computing (MEC) grows, understanding how mobile nodes interact within such distributed networks is crucial for ensuring efficient resource allocation and minimizing energy consumption. The research will analyze various mobility patterns and traffic models, exploring their impact on power usage and the ability to support near real-time edge computing services. The goal is to develop strategies for optimizing deployment, reducing energy consumption, and improving the quality of service, ensuring that edge networks can meet the demands of dynamic, mobile environments.
Learning outcome
By the end of the project, students are expected to:
- Understand the opportunity provided by 5G and Mobile Edge Computing to improve services while reducing energy consumption.
- Explore the trade-off between effectiveness and efficiency and develop heuristic algorithms for ideal deployment.
- Have a critical perspective on the promises and performance of 5G.
Conditions
We expect that you:
- have been admitted to a master's program in MatNat@UiO - primarily PROSA
- take this as a long thesis
- will participate actively in the weekly SINLab meetings
- include the course IN4230 in the study plan unless you have taken it at the bachelor level
- include the course IN5060 in the study plan, unless you have already completed a course on classical (non-ML) data analysis