Course teached as: B031370 - NETWORK APPLICATIONS Second Cycle Degree in INFORMATICS ENGINEERING Curriculum MULTIMEDIA SYSTEMS
Teaching Language
Italian/English (limited to specific teaching modules and activities).
Course Content
The course has strong interdisciplinary characteristics with the aim at integrating knowledge and skills acquired in other courses within the training proposal of this Master Degree in Informatics Engineering. The main contents of the course concern:
Support material available on the Moodle website of the course.
Learning Objectives
The course program aims to provide knowledge and expertise in the wide field of Information and Computing Technology (ICT). In particular, theoretical knowledge and engineering skills with interdisciplinary characterization in Modern Computer Networking technologies will be acquired in order to develop the knowledge and design, integration and innovation skills necessary in a constantly evolving sector.
Prerequisites
Knowledge of the basic concepts and princioples of Computer Networks.
Teaching Methods
In-class Lessons and Exercises. In-depth seminars held by qualified external experts.
Further information
In the new information age, networked systems have increasingly become pervasive and indispensable to human existence. The study of new research topics have led to accelerate the convergence and functional integration of knowledge of software engineering, advanced computing, Big Data, Artificial intelligence to name a few with issues specific to the Computer networks field. This course is designed to provide interested students to acquire knowledge and skills in an emerging and technologically advanced field to train a new figure of Computer Science Engineer who is in great demand today.
The course can be used in e-learning mode through the Moodle platform accessible from the University SIAF website.
The exam schedule is available at the UNIFI exam booking service.
Type of Assessment
The exam deals with an oral interview on all the topics covered by the course program.
In particular, the questions will be aimed at verifying:
- Knowledge of the systems models foreseen by the study program;
- Ability to apply theoretical models for the performance analysis of specific systems;
- Ability to formulate and solve problems related to the design of networks and their optimization in relation to specific requirements.
Course program
This course is designed to introduce advanced topics in computer networking. Application, network and access level algorithms and protocols will be covered, as well as experimentation, simulation and modeling techniques. The course explores the emerging challenges of research in the field of networks focused on the integration of computer knowledge with particular attention to the integration of new Artificial Intelligence methodologies in advanced networks and systems.
NOTE: The following must be considered as an tentative program to be confirmed at the end of the course.
List of topics:
• Basic skills of Queieng Syustems Theory;
• IoT and IIoT:
o Basic skills of manipulation and analysis of big data (eventual)
o Deep computation techniques for data prediction in intelligent systems (eventual)
o Real-time monitoring in IoT and IIot systems: Age of Information concept, Analysis model; applications to basic queiing systems , Examples of use cases.
• New Generation Wireless Networks:
o Massive access in 5G and subsequent networks: Analysis of the Aloha protocol and its evolutions; Recall of recurrent neural networks: structure, initialization and parameters. Application of AI principles in new generation access techniques.
o Service reliability: Service model for Virtual Reality
• Software Defined Networks
o Elements of Artificial Intelligence: objectives, methods and main classes;
o Artificial intelligence techniques for context-aware routing;
o Framework for forecasting network link congestion;
o Hierarchical machine learning for the detection of anomalies in the network.
• Network Function Virtualization: Slicing and Placement
o Elements of decision theory: basic definitions, game theory, matching theory and auction theory.
o The matching theory, the condition of stability and the concept of externalities
o The Gale-Shapley algorithm for the placement of virtual functions
o The auction algorithm for the definition of network slices and the Service Provider perspective
• Edge / Fog / Cloud
o Basic skills of distributed machine learning
o The problem of user privacy for the prediction of network services
o Federated Learning for content caching, computing and storage
o Markov decision-making and deep learning for the application migration problem
o Mobile Edge Computing: Markov analysis model with reneging of service requests, Comparison with realistic models.
• Channels Allocation in WPAN networks
o Channels Allocation through hypergraphs;
o Intelligent Radio basic on AI methgodologies.
• Software knowledge and practical activities
o Python calls
o Deep learning references (libraries and software)
o Command line and OpenCV
o Fundamentals of Edge-AI with OpenVINO
o Deep Learning Deployment for the IOT
o Software optimization tools and techniques for new services era applications.