BIG DATA SENSING, COMPRESSION AND COMMUNICATION

Learning Objectives

The course aims to provide students with some basics of information generation, encoding, compression and communication for big data scenarios.

Dublin Descriptors

1. Knowledge and understanding (Conoscenza e capacità di comprensione) - The course aims to provide students with knowledge and understanding of techniques and algorithms for acquisition and processing of data (e.g. sensor generated data, images, audio files) collected in smart environments such as in environmental monitoring, e-health, smart cities and/or vehicular scenarios. Then students will understand and study techniques for data compression both at the sources and, in a distributed way, in the network. Finally technologies and architectures for the transmission of big data will be studied.
2. Applying knowledge and understanding (Capacità di applicare conoscenza e comprensione) - After attending this course, students will be able to manipulate, process and reconstruct different types of data acquired from a smart environment, design compression algorithms suitable to perform data compression both at the data sources or into the network, choose and exploit the most appropriate set of technologies for data transmission in big data scenarios. Finally students will be able to solve specific big data design problems in realistic scenarios.
3. Making judgements (Autonomia di giudizio) - Upon completion of the course the students will gain independent and critical understanding skills as well as ability to discuss design aspects in real big data scenarios, commenting also on the design choices. Finally, at the end of the course, the students will be able to prosecute independently their study of other engineering-related disciplines with the ability to appropriately use big data design considerations in the appropriate context.
4. Communication skills (Abilità comunicative) - Students attending this course will learn to communicate and discuss/describe relevant Big Data application scenarios. Also they will be able to critically discuss and illustrate the most relevant design aspects to be taken into account upon focusing on generation, elaboration and communication of huge amounts of heterogeneous data like those generated in IoT networks.
   

The course consists of lectures and laboratory activity. The theorethical lectures are taught by the teacher while laboratory activities, consisting of exercises, will be carried out in collaboration by the teacher and by the students who are invited to solve, with the support of the teacher, exemplary problems. In addition, other lectures will be devoted to the illustration of software tools, e.g. Mathworks Matlab, useful for the solution of specific problems.

Should teaching be carried out in mixed mode or remotely, it may be necessary to introduce changes with respect to previous statements, in line with the programme planned and outlined in the syllabus.

Introduction (approx 3 hours): Introduction to Internet of Things-Introduction to big data-Definition of big data-Types of big data-operations on big data-Examples of big data.

Part 1 (approx 12 hours). Big data sensing: Types of data - Audio sources - Basics of acoustics - Human earing fundamentals - Basics of digital audio - Digital encoding - Sampling Theory - Different audio file formats - Compressed audio - Video sources - Basics of video encoding - Different video file formats - Multimedia transmission - Fundamentals - Jitter and synchronization - Multimedia file formats - Data sources - Data file formats - Examples of different mechanisms for data generation.

Part 2 (approx 10 hours). Big data compression: Source coding - Compressive sensing - Channel coding - Examples of compression techniques applied to different types of data.

Part 3 (approx 15 hours). Big data communication: Technologies for the IoT - WiFi - LoRa - SigFox - Examples of communication between nodes exploiting some of the technologies discussed above.

The following texts are suggested readings. During the course, the teacher can also suggest further readings (e.g. scientific papers and articles) on specific topics.

-A. Rezzani. Big Data Analytics: Il manuale del data scientist, Apogeo Maggioli Editore

-V. Lombardo, A. Valle. Audio e multimedia, 4th edition, Apogeo Maggioli Editore.

-Z. Han, H. Li, W. Yin. Compressive sensing for wireless networks. Cambridge University Press.

-F. Wu. Advances in visual data compression and communication: Meeting the Requirements of New Applications, CRC Press.

-U. Mengali, M. Morelli, Trasmissione numerica, Mc Graw Hill

The course consists of lectures and laboratory activity. The theorethical lectures are taught by the teacher while laboratory activities, consisting of exercises, will be carried out in collaboration by the teacher and by the students who are invited to solve, with the support of the teacher, exemplary problems. In addition, other lectures will be devoted to the illustration of software tools, e.g. Mathworks Matlab, useful for the solution of specific problems.

Should teaching be carried out in mixed mode or remotely, it may be necessary to introduce changes with respect to previous statements, in line with the programme planned and outlined in the syllabus.

Attending classes is not mandatory but strongly recommended.

The final exam will consist of a colloquium with the teacher on the topics dealt during the course. Learning assessment may also be carried out on line, should the conditions require it.