BIG DATA SENSING, COMPRESSION AND COMMUNICATION

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

1. Knowledge and understanding  - 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 - 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 - 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 - 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 teachers while laboratory activities, consisting of exercises, will be carried out in collaboration by the teachers and by the students who are invited to solve, with the support of the teachers, exemplary problems. In addition, other lectures will be devoted to the illustration of software tools useful for the solution of specific problems.

1. 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.
2. Big data sensing (approx 10 hours): 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.
3. Big data compression (approx 10 hours): Source coding - Compressive sensing - Channel coding - Examples of compression techniques applied to different types of data.
4. Big data communication (approx 17 hours): Technologies for the IoT - WiFi - LoRa - SigFox - Software Defined Radio - Examples of communication between nodes exploiting some of the technologies discussed above.

The following texts are suggested readings. During the course, the teachers 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

• Oral exam
• In addition to the oral exam, students may present and discuss a paper on a topic agreed upon with the professors (Optional)