In Sintesi

Area Riservata

Security in Computing Systems


Credits: 9

Objectives: The objective of this course is to introduce the theorethical background and the basic methodologies and solutions to develop secure distributed applications. The course enables the students to design and implement secure distributed applications in several application domains including electronic payments, enterprise and e-health.

Description: This course is organized in three parts addressing different issues .

  • Part 1 (algorithms). Applied cryptography. Ciphers (symmetric and asymmetric), hash functions (MDC and MAC), digital signatures, secure random number generators. Analysis of security and performance. Particular reference to specific cryptographic schemes including AES, RSA and ElGamal 
  • Part 2 (protocols). Identification, authentication, key establishment. Certificates and public key infrastructures. Biometry. Analysis and design of cryptographic protocols. The BAN logic. 
  • Part 3 (applications). Secure application programming and secure web programming. Secure multimedia distribution. Electronic payments. Privacy. 

During the course, case studies will be presented and analysed.

The Lab project consists in the design and implementation of algorithms covering topics developed during the course.

Assessment: Written exam + Lab project

Prerequisites: Basic notions of security including access control and firewalls.

Modules: 1

Module 1 (9 Credits): Security in Computing Systems


Networked Embedded Systems


Credits: 9

Objectives: The objective of this course is to teach the theoretical background and the basic methodologies for developing networked embedded systems and applications. The course enables the students to design and implement applications, based on wireless sensor networks and networked embedded devices, in several application domains, including environmental and structural monitoring, e-health, logistics, smart buildings, etc.

Description: This course is organized in three sections addressing different issues in the design of networked embedded systems and applications.

The first section covers architectural issues. The most common architectures and communication protocols for wireless sensor networks are surveyed. Available solutions for energy harvesting, energy conservation, and energy efficiency are also presented.

The second part addresses software issues. The main software platforms currently available are surveyed, with special focus on the TinyOS/NesC platform. Middleware solutions are also presented. A number of case study applications based on wireless sensor networks are discussed.

The third section addresses methodologies for managing the complexity of data and developing context-aware applications. Data compression and aggregation algorithms suitable for tiny sensor nodes and solutions for context modeling and representation are presented. Relevant case-study applications are also discussed.

The Lab project consists in the design and implementation of a simple application, based on wireless sensor networks, under the TinyOS/NesC platform.

Assessment: Lab projects and oral test.

Prerequisites: Mobile and Pervasive Computing

Modules: 1

Module 1 (9 Credits): Networked Embedded Systems


Performance Evaluation of Computer Networks and Systems


Credits: 9

Objectives: Main aim: modelling and analysis of queuing systems, with applications in networking, computers, and services. Topics include discrete-time and discrete-state Markov chains, birth-death processes and simple Markovian queues, networks of queues and product form networks, single and multi-server queues. The course will cover state of the art results leading to research opportunities. Discrete-time simulation will be also thoroughly developed as a technique for assessing the performance of computers, networks and services.

Description: The course is in three parts.

  • Part 1: review of all the prerequisites from applied probability that are needed for a basic course in queuing systems. The concepts of unconditioned and conditioned probability will be introduced, along with the principles of counting. In addition, the most common random variables will be dealt with, along with the concepts of expectation, variance, single and joint distribution. 
  • Part 2: Explores both the theory and application of fundamental and advanced queuing models. Fundamental models include single and multiple server Markov queues, bulk arrival and bulk service processes, and priority queues. Applications emphasize computer networks and computer operations, but may include examples from other fields. Advanced topics may vary. 
  • Part 3: The basic principles of discrete-event simulations, covering simulation workflow, data structures used in simulation, random variable generation, transient elimination and output data analysis. Part of the module will focus on describing a well-known simulation framework and experimenting with it.

Assessment: Written and oral tests.

Prerequisites: None

Modules: 1

Module 1 (9 Credits): Performance Evaluation of Networks and Systems


Mobile and Pervasive Computing


Credits: 6

Objectives:The course enables the students to understand the properties, architectures and application areas of mobile and pervasive distributed systems, and to learn the basic methodologies for designing applications using different mobile and pervasive platforms.

Description:The course is organized in lectures and laboratory sessions. Lectures will address architectural and operating system issues, middleware abstractions and mechanisms, mobile and pervasive programming paradigms (including event-driveness, power-awareness, context-awareness, pervasive sensing). In laboratory sessions students will exercise with mainstream technologies for designing smartphones-based client applications and wireless sensing applications. Mobile applications and their integration with the back-end servers will be developed using different platforms (e.g., JME, Android, iOS).

Assessment: Oral exam and Lab project.

Prerequisites: To be defined

Modules: 1

Module 1 (6 Credits): Mobile and Pervasive Computing


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