Thesis subject

Use Machine Learning for Processing Data of Remote Sensing

Wireless sensor networks are widely used in various applications for data collection. With the improvement of hardware, the data size collected by these sensors increases significantly. In this condition, the traditional solution for collecting and processing sensing data is no longer suitable.

In this project, we aim to build a demonstration proof-of-concept system to realize a remote sensing application, such as Internet of Nose, in an agricultural environment. In this system, we assume that sensor nodes are deployed on ground static and mobile objects (emulating plants and flocks of animals) to monitor surrounding smells. The task is to utilize a machine learning solution to process the data collected from wireless networks.

This project is a component of a cooperative project of TU/e, TUDelft, and WUR. In the component of WUR, we aim to provide a machine learning solution that can be used for a wireless mesh network. Specifically, the following tasks are in this project:

  • Identify variables of interest within an agricultural application, such as temperature.
  • Identify the key requirements to wireless sensing that are related to data processing.
  • Use a machine learning solution to process the data collected from the agricultural application.

The content above is an overall description of the project. The detailed research work of the project could be based on further discussion between supervisors and students.


    Requirements (optional)

    • Pyhon programming
    • Machine learning

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