84237 - Wireless Networks for the Internet of Things (2nd cycle)

Academic Year 2018/2019

  • Docente: Davide Dardari
  • Credits: 6
  • SSD: ING-INF/03
  • Language: Italian

Learning outcomes

At the end of the course, students will be able to design wireless networks for the Internet of Things and, more in general, in the field of cyber-physical systems enabling the monitoring and control of physical processes. Specifically, they will study low-complexity wireless communications, network connectivity analysis tools, energy efficient networking and synchronization algorithms as well as distributed signal processing methods applied to wireless sensor networks.

Course contents

  • Introduction to the requirements and technologies for the Internet of Things, with particular reference to wireless sensor networks.
  • Short-range communication techniques - connectivity models - energy efficient routing and synchronization protocols.
  • Distributed detection and estimation of physical entities.
  • Bayesian methods for optimal estimation and tracking (Bayesian filters, Kalman filter).
  • Case study examples and laboratory activity on the design of wireless networks.

Readings/Bibliography

The slides adopted during the frontal lesson will be made available to students through Internet.

The acquisition of dedicated books is not required.

 

Bibliography for further deepening:

R. Verdone, D. Dardari, G. Mazzini, A. Conti "Wireless sensor and actuator networks: technologies, analysis and design", Ed. Elsevier, 2008.

Teaching methods

The course consists of 6 CFU of which 3 CFU of frontal lessons and 3 CFU (30 hours), of laboratory group experiences on projects identified by the students.

The laboratory is equipped with 5 development kits for wireless sensor networks.

The objective of practical activity is twofold:

- acquire confidence with typical development tools for wireless networks;

- acquire the skills to carry out individual and team design activities.

 

Assessment methods

A comprehensive oral exam will assess skills acquired during the course and during the group laboratory activity. 

Optionally, students have the faculty to prepare and discuss a simulation project, previously agreed with the supervisor.

Teaching tools

The slides adopted during the frontal lesson will be made available to students through Internet.

Matlab environment.

Laboratory equipped with 5 development kits for wireless sensor networks.

Office hours

See the website of Davide Dardari