94025 - Wireless Networks for the Internet of Things (2nd cycle)

Learning outcomes

At the end of the course the student can design wireless networks for the Internet-of-Things (IoT) and, more in general, wireless networks in the framework of cyber-physical systems for physical processes monitoring and control through embedded systems. In particular, the student acquires knowledge of the main low complexity transmission techniques, wireless connectivity models, energy-efficient networking algorithms, statistical signal processing of sensor data, and information fusion.

Course contents

Throughout the course, the following topics are addressed:

• Channel models for IoT and short range communications; link budget for IoT systems in real environments.
• Poisson point processes.
• Connectivity theory in wireless networks. Single link connectivity: deterministic and statistic models. Multi-hop connectivity: Galton-Watson model. Network connectivity: graphical model and Fiedler value, Penrose theorem, giant component.
• Multiple access techniques for IoT. Coordinate multiple access techniques (hints). Uncoordinated multiple access: Aloha, Aloha Slotted, Carrier sensing multiple access (CSMA).
• Interplay between Internet-of-Things and cyber-physical systems. Bayesian recursive estimation: Bayes filter and its implementations, Kalman filter.
• Analysis of the main technologies for IoT. Proprietary systems and 3GPP standardization of IoT technologies in the framework of 4G/5G cellular networks.

Readings/Bibliography

The following books are useful for consultation and in-depth analysis purposes:

• D. Bertsekas, R. Gallager, Data Networks (2nd edition). Pearson, 1992.
• R. Mahler, Statistical Multisource Multitarget Information Fusion. Artech House, 2007.

Teaching methods

The course is organized in classroom frontal lectures in which the fundamental elements of IoT systems are addressed. The theoretical investigation of each theme is combined with examples and with the resolution of specific problems and exercises, with the purpose to illustrate and remark the practical applications.

In the framework of some of the covered topics the implementation of a software simulation (Matlab) is proposed, aimed at performance evaluation with numerical methods.

Assessment methods

Examination consists of an oral exam, aimed at assessing the methodological capabilities and the level of knowledge of the covered topics gained by the student.

Students who will exhibit a full and comprehensive knowledge of all class topics, together with the capability of using them in the framework of new problems, and an excellent command of language, will receive an evaluation ranging from good to excellent. Students whose knowledge of the covered topics is mostly mnemonic and whose language is essentially correct but not always appropriate will receive a grade of fair. Possible knowledge gaps, ignorance of the definition of important quantities or parameters, or an inappropriate and lacking language will receive a negative rating.

Teaching tools

Educational material: slides covering all topics are made available to the students in electronic format in the institutional repositories.

Office hours

See the website of Enrico Paolini