Professors: Michela Meo
Official description of the course: here
The course is organized in three main parts:
- technologies for data transport and distribution in smart environments
- methodologies for the network design, planning and performance evaluation
- laboratory experience
More in details, the course first provides a general overview of the technologies for data transport and distribution, with a particular attention to smart environments that require the management of large amounts of heterogeneous data. Then, the course focuses on some of the main methodologies used for the design, planning and performance evaluation of data distribution networks. Both analytical modelling (based on queuing theory and random graph theory) and simulation techniques are presented and applied to practical use cases. The lab experience is mainly devoted to simulation techniques (in python) and consists in a practical experience of design or performance evaluation of a smart network.
The course is long (it is annual, 12 credits), the title is long (I even don’t really understand it) but it is very nice and usually students like it. I also like to teach it. Idilio Drago is doing a great job teaching part of the course and managing the lab experience.