Master theses

Intelligent Sensors

Keywords: Artificial Intelligence, Big Data, cloud computing, AI, ML, Machine Learning, Data Streams, Internet of Things, Apache Flink, IoT, Streams, frameworks

Supervision: Ben De Meester Anastasia Dimou

Students: max 1

Internet of Things (IoT) comes with challenges on managing knowledge coming from data streams! For instance, in a smart space set up, multiple sensors provide large volumes of data in frequent intervals about temperature, humidity, air quality etc. However, it is required to understand the context and relationships of the data to build intelligent applications.

You will work on architectures (e.g., hoodie by Uber [https://eng.uber.com/hoodie/]) and frameworks (e.g., Apache Spark [https://spark.apache.org], NiFi [https://nifi.apache.org/] and Kafka [https://kafka.apache.org/]) for processing data streams and exposing the knowledge they carry. The goal is to orchestrate devices, data, streams, and other resources into a rich and coherent experience able to manage multiple, heterogeneous devices over multiple, heterogeneous networks, and create a single experience that enables scalability and effectiveness. Experiments can run on Antwerp’s City of Things infrastructure, Gent’s Smart Spaces or European Mobility data.

In this master thesis, you will answer questions like how can we integrate data from heterogeneous streams? how can we increase the performance of their processing? By the end of this master thesis, you will know how to deal with processing and integration of streaming data, and different frameworks for streaming data and distributed processing.