Data is growing and has grown very fast in the last years.”Big Data” analytics is challenging today because of the unprecedented large data volumes. In this course, we will describe the structure of data generated in big data sensing applications, by distinguishing the type and structure of data. Then we will discuss SoA methodologies which can be used to compress this data based on its intrinsic features; finally, communication protocols for remotely delivering this data will be described and detailed. In this way students will be provided with communication engineering competences allowing them to actively communicate with experts in various fields by providing focused and competent data analysis for every application, such as in scientific, technological or business fields. Students will also be able to exploit the competences gained for design processes of collection, compression and communication of heterogeneous big data. All in all, this is a fundamental course to well understand the intrinsic nature of IoT big data.

Learning Objectives

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

Dublin Descriptors

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.

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.

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.

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 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, e.g. Mathworks Matlab, useful for the solution of specific problems. Also a practical project will be assigned by the teachers and developed in teams by students during the course.

Basics of maths (integrals, derivatives, matrixes, vectors, functions, scientific/exponential notation), basics of communication systems (not strictly required).

Attendance is required. Students should attend at least 70% of the course lectures and at least 70% of the lectures devoted to the preparation of the project.

Introduction (2 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 application scenarios.

 
Part 1 (20 hours). Big data communication: Technologies and architectures for the IoT - Bluetooth LE-RFID - 6LowPAN - IEEE 802.15.4 - WiFi - ZigBee - LoRa - SigFox – Software Defined Radios - Examples of communication between nodes exploiting some of the technologies discussed above.
 
Classwork (20 hours). Design and implementation of a data communication system for Big Data transmission.

 
Part 2 (20 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 - Image sources - Basics of image encoding - Different image file formats - Video sources - Basics of video encoding - Different video file formats - Multimedia transmission - Fundamentals -  Multimedia file formats - Data sources - Data file formats 
 
Part 3 (17 ore). Big data compression: Source coding - Compressive sensing - Channel coding – Lossless and Lossy compression techniques - Examples of compression techniques applied to different types of data.

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.

-Scientific papers in the field suggested by the Teachers

The final exam will include an oral colloquium on the topics dealt during the course. Also a project will be developed by students in teams during the course and the final exam will include a presentation of the team about the activities and the final evaluation of the Teachers about this project.

1. Loss Less compression techniques, entropy considerations
2. Lossy compression techniques, entropy considerations
3. Huffman encoding
4. Shannon Fano encoding
5. Arithmetic encoding techniques
6. Transform encoding techniques (DCT and Wavelet)
7. Audio encoding
8. Video encoding and standards
9. Image encoding (JPEG and JPEG2000)
10. IEEE 802.11 and WiFi
11. IEEE 802.15.4 and ZigBee
12. Bluetooth and BLE
13. SigFox
14. LoRa