M.Sc. Topic / Guided Research: Proximity Services in Highly Mobile Environments
There is a trend to enrich our environment with a multitude of sensors to enable new services based on sensor data. In this context, we have a lamppost, which is equipped with different sensors to detect temperature, light intensity, pressure, humidity, and infrared. One of the uses cases is black ice recognition.
The master thesis is settled in the field of Vehicle-to-Everything Communication (V2X Communication). The lamppost provides wireless access to nearby entities, which are in reach of the antenna. Therefore, the lamppost act as a gateway to provide different proximity services, such as a messaging system. The service discovery must be fast due to time and distance restriction to ensure sufficient bandwidth for data transmission within connection time. It is important that the system works securely and addresses different threats. The use case considers a messaging system with different messaging types for data payload and information sensitivity. The goal is to inform nearby car drivers about road conditions (e.g., black ice), car accidents, blocked streets or free parking spots.
Goals and Objectives
- Two entities implemented as virtualized Docker container (in Java or Python)
- Lamppost that serves as service gateway
- Car which uses the service
- ONE simulator for networking
- SUMO simulator for traffic conditions
- Evaluation parameters
- Service discovery and network association time
- Amount of transferred data
- Service delay due to lamppost hopping
- Impact of weather conditions like rain modeled as additional delay in wireless communication
- WiFi authentication fast enough for proximity services in highly mobile environments?
- How much data can be transferred during connection to the lamppost?
- Simulation with different bandwidth and driving speed
- How can we model the smooth transition from one lamppost to another, if the connection is lost to the lamppost during the drive
- What is the impact of security in this scenario under the aspect of real-time processing
- Performance impact: latency, throughput
- System impact: implementation complexity
Michael Haus (M.Sc.), haus at in tum de