Guided Research / M.Sc. Topic: Indoor Localization via Decentralized Light Bulb Network

System overview for indoor localization
Light bulb
Light bulb components

 

Visible light communication (VLC) has been enabling many applications related to IoT, such as accurate indoor localization, gesture recognition, activity detection, occupation detection. We have created a custom light bulb with a 3D printed case to be embedded into the existing lighting infrastructure. The light bulb contains an IoT board, e.g., BeagleBone Black to control the high power LED to transmit information via visible light. It transmits data by switching the current of the LEDs on and off at high frequency so that the switching effect is too quick to be noticed by the human eyes. In addition, our light bulbs support offline working mode in case of missing external power. This allows further administration of the light bulbs as long as the integrated battery powers the IoT board. The design overview shows the system for indoor localization. We are using the light bulbs as anchor points to infer a user's location indoors. For example, one use case is to guide the user to a specific or nearest printer.

Goals

  • Create a testbed for indoor localization including deployment of light bulbs
    • Questions
      • To which ceiling heights the system is usable, e.g., 10 m?
      • At which times the system is useable, e.g., intensity of ambient light?
  • Create a framework for light bulb administration to change transmitted information and transmission frequency
  • Implement the indoor localization depending on the received information from the light bulbs
    • Questions
      • Which localization accuracy is achievable depending on the density of light bulbs?
      • How to model the transition phase when the light signal is received from two light bulbs?

Requirements

  • Good programming skills, e.g., Python, Java
  • Linux skills, e.g., loading kernel modules, existing VLC system is based on real-time Linux kernel modules

Contact

If you are interested into the thesis, please send me your CV and exams transcript.

Michael Haus - haus@in.tum.de