BIG DATA SENSING, COMPRESSION AND COMMUNICATION

General Objectives

• Understanding the Big Data paradigm and techniques: Equip students with a foundational understanding of techniques and algorithms for data acquisition, processing, compression, and communication in big data scenarios, focusing on data collected from various smart environments.
• Developing Big Data systems: Develop students' ability to apply acquired knowledge and techniques to manipulate, process, and reconstruct diverse types of data acquired from smart environments. Enable them to design effective compression algorithms and select appropriate technologies for data transmission in big data scenarios.
• Applying Critical Thinking to Big Data scenarios: Foster independent and critical thinking skills among students, enabling them to evaluate design aspects and make informed decisions in real big data scenarios. Encourage discussion and analysis of design choices to enhance understanding and judgment.
• Learning the Modus Operandi behind Big Data systems: Empower students to integrate big data design considerations into other engineering-related disciplines independently. Enable them to apply their knowledge and skills across various contexts, ensuring the relevance and applicability of big data principles beyond the scope of the course.

Synthetic General Description

This course provides students with a comprehensive understanding and practical skills in handling big data in various smart environments. Students will learn the fundamentals of information generation, encoding, compression, and communication within big data contexts. Through theoretical exploration and hands-on applications, they will acquire knowledge of techniques and algorithms for data acquisition, processing, and compression. Additionally, students will delve into the intricacies of data transmission technologies and architectures. By the course's conclusion, students will possess the capability to manipulate, process, and reconstruct diverse data types from smart environments. They will be adept at designing compression algorithms and selecting appropriate transmission technologies for big data scenarios. Moreover, students will develop critical thinking and judgment skills, enabling them to evaluate design aspects and make informed decisions in real-world big data scenarios. Ultimately, this course empowers students to independently apply big data design considerations across various engineering disciplines, enhancing their adaptability and problem-solving abilities in complex contexts.

Expected Learning Results

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

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.

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.

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.

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.

·   Introduction (1 CFU)

o   Introduction to Internet of Things

o   Introduction to big data

o   Definition of big data

o   Types of big data

o   Operations on big data

o   Examples of big data

·   Big data sensing (2 CFU)

o   Types of data

o   Audio sources

o   Basics of acoustics

o   Human earing fundamentals

o   Basics of digital audio

o   Digital encoding

o   Sampling Theory

o   Different audio file formats

o   Compressed audio

o   Video sources

o   Basics of video encoding

o   Different video file formats

o   Multimedia transmission Fundamentals

o   Jitter and synchronization

o   Multimedia file formats

o   Data sources

o   Data file formats

o   Examples of different mechanisms for data generation.

o   Laboratory activities on the analysis and processing of audio signals, images and time series data

·   Big data compression (1 CFU)

o   Source coding

o   Compressive sensing

o   Channel coding

o   Examples of compression techniques applied to different types of data.

o   Laboratory activities on the application and performance comparison of different data coding and compression techniques (audio and images)

·   Big data communication (2 CFU)

o   Technologies for the IoT

o   IoT architectures

o   LoRa

o   SigFox

o   MQTT protocol

o   Software Defined Radio

o   Laboratory activities on the communication between nodes exploiting some of the technologies discussed above (e.g., Software Defined Radio, LoRa)

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

Marks:

• Failed: the student does not know the basic concept of the course and has completed less than 40% of the required assignments
• 18-20: the student has a basic knowledge of the topics of the course but he has great difficulties in applying them to practical exercises and problem solving pipelines.
• 21-24: the student has a basic knowledge of the topics of the course and he is able to solve simple problems and exercises with some guidance from the teacher.
• 25-27: the student has a good knowledge of the topics of the course and can complete the assignment in autonomy with minor errors
• 28-30 e lode: The student has full knowledge of the topics of the course and is able to complete in autonomy assignments making connections and with only very minimal occasional mistakes.