Educational offer

  
 
First cycle
degree courses		 Second cycle
degree courses		 Single cycle
degree courses
School of Engineering
ICT FOR INTERNET AND MULTIMEDIA
Course unit
NETWORK ANALYSIS AND SIMULATION
INP3049930, A.A. 2018/19

Information concerning the students who enrolled in A.Y. 2018/19

Information on the course unit
Degree course Second cycle degree in
ICT FOR INTERNET AND MULTIMEDIA
IN2371, Degree course structure A.Y. 2017/18, A.Y. 2018/19
N0
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Degree course track INTERNATIONAL MOBILITY [005PD]
Number of ECTS credits allocated 6.0
Type of assessment Mark
Course unit English denomination NETWORK ANALYSIS AND SIMULATION
Department of reference Department of Information Engineering
Mandatory attendance No
Language of instruction English
Branch PADOVA
Single Course unit The Course unit can be attended under the option Single Course unit attendance
Optional Course unit The Course unit can be chosen as Optional Course unit

Lecturers
Teacher in charge MICHELE ZORZI ING-INF/03

Mutuating
Course unit code Course unit name Teacher in charge Degree course code
INP3049930 NETWORK ANALYSIS AND SIMULATION MICHELE ZORZI IN2371

ECTS: details
Type Scientific-Disciplinary Sector Credits allocated
Core courses ING-INF/03 Telecommunications 6.0

Course unit organization
Period Second semester
Year 1st Year
Teaching method frontal

Type of hours Credits Teaching
hours		 Hours of
Individual study		 Shifts
Lecture 6.0 48 102.0 No turn

Calendar
Start of activities 25/02/2019
End of activities 14/06/2019

Examination board
Board From To Members of the board
2 A.A. 2018/2019 01/10/2018 15/03/2020 ZORZI MICHELE (Presidente)
ZANELLA ANDREA (Membro Effettivo)
BADIA LEONARDO (Supplente)
CALVAGNO GIANCARLO (Supplente)
CORVAJA ROBERTO (Supplente)
ERSEGHE TOMASO (Supplente)
LAURENTI NICOLA (Supplente)
ROSSI MICHELE (Supplente)
TOMASIN STEFANO (Supplente)
VANGELISTA LORENZO (Supplente)
1 A.A. 2017/2018 01/10/2017 15/03/2019 ZORZI MICHELE (Presidente)
ZANELLA ANDREA (Membro Effettivo)
BADIA LEONARDO (Supplente)
CALVAGNO GIANCARLO (Supplente)
CORVAJA ROBERTO (Supplente)
ERSEGHE TOMASO (Supplente)
LAURENTI NICOLA (Supplente)
MILANI SIMONE (Supplente)
ROSSI MICHELE (Supplente)
TOMASIN STEFANO (Supplente)
VANGELISTA LORENZO (Supplente)
ZANUTTIGH PIETRO (Supplente)

Syllabus
Prerequisites: The course requires preliminary knowledge of: Mathematical Analysis, Probability, random variables and random processes, networks and protocols. It also assumes a minimum level of programming experience and a basic knowledge of some language (eg, MATLAB, C, Python).
Target skills and knowledge: The training objective of the course involves the acquisition of the following knowledge and skills:

1. Understanding the main problems related to simulation and performance analysis through random computer experiments
2. Understanding the probabilistic fundamentals underlying the theory of statistical confidence
3. Knowing how to design simple simulation programs, write the relative code, and be able to correctly interpret and represent the results obtained
4. To gain experience with a complex network simulator (Omnet++) through appropriate laboratory experiments
5. To demonstrate the knowledge of the methodologies of the course and the ability to use a simulation tool to solve a problem of interest, presenting and discussing its formulation and results
Examination methods: The assessment of the knowledge and skills acquired is carried out through the development and presentation of a project. About half way through the course, students are offered some possible topics for a project to be developed by the end of the course, individually or in groups of 2 or 3 people. The project, which should engage every student for 40-50 hours in total, must be illustrated in a written document and presented with slides. The presentation and discussion of the project, possibly supplemented by a discussion of the homeworks, constitutes the final exam.
Assessment criteria: The evaluation of the acquired knowledge and skills will be carried out considering:

1. The completeness and depth of the knowledge of the topics covered during the course.
2. The ability to apply the theoretical concepts treated during the course to specific practical problems
3. The ability to obtain correct numerical results in the proposed exercises
4. The ability to develop, describe and present the final project.
Course unit contents: 1. Introduction to simulation, methodologies
2. Data analysis, confidence intervals for various metrics
3. Random number generators and their properties
4. Generation of random variables with any distribution
5. MonteCarlo simulations, dynamic simulations, event-driven simulations
6. Variance reduction techniques
7. Calculation of complex integrals with Gauss quadrature formulas
8. Application examples: SIR in cellular systems, Slotted ALOHA on radio channel, performance of a geographic routing protocol
9. Various simulations using Omnet ++ (laboratory)
Planned learning activities and teaching methods: The course is made of 32 hours of lectures and 16 hours of laboratory activities.

Some classes are carried out using slides, which makes it easier to use complex figures. The more theoretical classes are instead carried out on the blackboard, as it is believed that this method of delivery allows to maintain the right rhythm of presentation of the topics and to keep the students more engaged. In any case, we will try to maintain an interactive teaching style, stimulating students to intervene and discuss with the teacher and with each other.

The laboratory activities are based on the Omnet++ simulator and require the student to simulate progressively more complex systems and to perform simple parametric studies through simulation.

The course includes four mandatory homeworks, which consist mainly of programming exercises and visualization of results, to verify the acquisition of the concepts developed in class and to stimulate the students to carry out practical activities.

Finally, the course includes the development of a final project whose presentation and discussion constitutes the final exam.
Additional notes about suggested reading: The teaching material, including all the transparencies used in class, the texts that deal with the topics developed, scientific articles covered in class, exercises, datasets to be displayed, is entirely available on the website of the course on the e-learning platform.
Textbooks (and optional supplementary readings)
  • J.Y. Le Boudec, Performance Evaluation of Computer and Communication Systems. Lausanne, Switzerland: EPFL Press, 2010. Cerca nel catalogo
  • S. Ross, Simulation. --: Wiley, 2006. 4th ed. Cerca nel catalogo
  • A.M. Law, Simulation Modeling and Analysis. --: McGraw-Hill, 2006. 4th ed. Cerca nel catalogo

Innovative teaching methods: Teaching and learning strategies
  • Lecturing
  • Laboratory
  • Problem based learning
  • Working in group
  • Problem solving
  • Loading of files and pages (web pages, Moodle, ...)
  • Omnet++ network simulator

Innovative teaching methods: Software or applications used
  • Moodle (files, quizzes, workshops, ...)
  • Matlab

Sustainable Development Goals (SDGs)
Quality Education Industry, Innovation and Infrastructure

 
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