Objective
Improve our understanding of service discovery mechanisms for IoT devices in scenarios such as search and rescue and disaster relief, where we cannot rely on the availability of Internet and Cloud access.
The technologies that will be investigated as both WiFi and 5G-SA (standalone), and the exploration of using them in combination.
Project Description
This thesis will explore efficient service discovery mechanisms in distributed IoT networks, leveraging emerging technologies such as 5G, Wi-Fi, and embedded systems like Raspberry Pi. With the rapid growth of connected devices, it is essential to develop scalable, low-latency solutions that can enable seamless discovery of services in dynamic environments. The research will focus on improving the scalability, security, and reliability of service discovery protocols, integrating various communication technologies to optimize connectivity in heterogeneous IoT networks. The outcome aims to enhance performance in real-time applications, particularly in smart cities, industrial IoT deployments, and critical areas such as search and rescue operations, crisis management, and disaster relief, where rapid, reliable service discovery will be key to establishing ad-hoc support services, building situational awareness, and managing the fluid situation.
Learning outcome
By the end of the project, students are expected to:
- Experience in developing networked applications on low-end devices such as the Raspberry Pi.
- Understand distributed systems, and the requirements of IoT systems in particular.
- Understand the networking and management options of both 5G and WiFi.
- Develop a solution that can work around service restriction in 5G networks in particular.
- Have a critical perspective on the promises and performance of 5G and WiFi.
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 IN5020 in the study plan
- include the course IN5060 in the study plan, unless you have already completed a course on classical (non-ML) data analysis