How much Data Movement is Being Saved?

In this project, we would specifically like to quantify the data movement savings of applying techniques like compression and model-based data filtering in the context of resource-constrained hardware and edge/IoT applications.

Today many data sources are small low-powered and hardware-constrained devices such as mobile phones, wearable or self-driving smart platforms, etc. Processing the data on these platforms instead of always sending it to the cloud for further processing would reduce data movement. This, in turn, would reduce latency, costs, and power required to deploy data-intensive applications at the edge. However, the impact of different ways of processing the data on data movement hasn’t been thoroughly investigated.

If you are interested in resource-constrained hardware, benchmarking, and machine learning in general, this project would be a great fit for you.

This project would be suitable as a standalone project or BSc or MSc thesis at ITU during Fall 2024. Depending on the size of the project and interests of the students, we can adjust the number of application domains to investigate.