UE Network applications

Informations aux utilisateurs

Veuillez noter que vous consultez une page du catalogue de formation 2020-2021. Le recrutement est actuellement terminé pour les licences, licences professionnelles, masters, DUT et formations réglementées de santé. Pour consulter le catalogue des formations 2021-2022, cliquez sur le lien suivant.

Diplômes intégrant cet élément pédagogique :


Security of Network and Applications (18h + 8h labs)

The objective of this class is to introduce security principles, on the theoretical, organizational and technical aspects. The points which are more specifically developed are: detection errors, firewall technics, network architecture, cryptology and VPN, anti-virus strategy. Are also discussed how to implement a security strategy, and some elements for the definition of a security policy. Some elements about safe networks, or networks for safety or critical applications, are also studied.

Lesson Topic 
Introduction to networks, error detection and correction 
 Bases of network, theoretical elements of error correction and detection, application in the case of parity, CRC, checksum.
Dependability - security - risk analysis 
 Concepts, application to networks and information systems, simple application examples. 
Attack strategies 
 The phases of an attack, types of attacks. 
Technologies for security:
 Network infrastructure, filtering, security protocols, VPN. 
 Theories on symmetric and asymmetric cryptography, DES, RSA, application to encryption, hash calculation, signature, certificates.
 Bases of virology. application to encryption, hash calculation, signature, certificates. 
Lab 1 Firewalls and wireless networks 
Lab 2 Communication security and encryption 

Field buses and Zigbee (10.5 h + 15h labs)

Distributed Algorithms and Network Systems (13.5 h + 6h labs)

Objectives Distributed algorithms aim at obtaining a global goal by exploiting a large number of simple devices (``agents''), and their local interactions. These algorithms can be for the purposes of estimation in a wireless sensor network, or control e.g. of a self-organized robotic fleet. This introductory class will first review the necessary tools from graph theory and Markov chains, and then present consensus: a prototypical example of distributed algorithm, as well as a building block for more complex algorithms. Theory will be accompanied by implementation on a real-world sensor network: FIT/IoT LAB.Class schedule 

  1. Introduction: network systems 
  2. Graphs: fundamentals of algebraic graph theory
  3. Markov chains: convergence to invariant measure, Perron-Frobenius theorem 
  4. Consensus (time-invariant graph) 
  5. Consensus (gossip: randomly varying graph) 
  6. Consensus-based algorithms: using consensus as a building block of other algorithms (e.g., localisation from relative measurements, least-squares regression, gradient descent minimization, distributed Kalman filter, counting nodes in an anonymous network) 
  7. Labs (3): implementation of distributed algorithms on real sensor network, remotely using FIT/IoT LAB. Programming language: C.



Informations complémentaires

Lieu(x) : Grenoble
Langue(s) : Anglais