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Difference between revisions of "Implementing A Low-Power Sensor Node Network"

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At the end of the project, you have to present/defend your work during a 15 min. presentation and 5 min. of discussion as part of the IIS colloquium.
 
At the end of the project, you have to present/defend your work during a 15 min. presentation and 5 min. of discussion as part of the IIS colloquium.
  
[[Category:Digital]] [[Category:Available]] [[Category:Semester Thesis]] [[Category:Master Thesis]] [[Category:Bachelor Thesis]] [[Category:Event-Driven Computing]] [[Category:Hot]]
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[[Category:Digital]] [[Category:Semester Thesis]] [[Category:Master Thesis]] [[Category:Bachelor Thesis]] [[Category:Event-Driven Computing]] [[Category:Hot]]
 
[[Category:Scheremo]]
 
[[Category:Scheremo]]

Latest revision as of 14:53, 11 October 2021

Introduction

In the current age of pervasive computing, the most common processing platforms are not computers anymore, but tiny, embedded microcontrollers. Leveraging these platforms for distributed computing is a topic of on-going research. One of the key issues in enabling deployment of such Internet of Things devices is communication. While many key technologies ranging from Low-Energy Bluetooth to LoRa have been proposed, there is still a lack of low-power optimized demonstrator platforms.

Project description

In this project, a sensor node platform with long-range communication will be developed. Starting from existing designs that integrate LoRa, the student's task is to integrate an embedded camera module and design a PCB to host all of the components. Depending on the student's progress, further tasks are to acquire an in-field dataset and training of a CNN to detect activity.

The student is required to:

  1. Read up on the LoRa protocol
  2. Deploy the protocol with a gateway
  3. Attach an embedded camera
  4. Design a PCB that hosts all components
  5. Prepare a demo

Depending on the progress, the following points may be investigated:

  1. Acquistion of a dataset
  2. Training and deployment of an energy-proportional CNN

Required Skills

  • Basic knowledge of the C language and embedded system programming

Skills you might find useful, but are not required:

  • Previous experience with the LoRa stack
  • PCB Design with Altium
  • Machine learning with Python
  • Neural network deployment on embedded devices (Machine Learning on Microcontrollers)

Professor

Luca Benini
Status: Available

Possible to complete as a Master, Semester or Bachelor Thesis

Supervision: Moritz Scherer scheremo@iis.ee.ethz.ch, Michele Magno magnom@pbl.ee.ethz.ch

Meetings & Presentations

The students and advisor(s) agree on weekly meetings to discuss all relevant decisions and decide on how to proceed. Of course, additional meetings can be organized to address urgent issues. At the end of the project, you have to present/defend your work during a 15 min. presentation and 5 min. of discussion as part of the IIS colloquium.