BIG DATA SENSING, COMPRESSION AND COMMUNICATION
Academic Year 2024/2025 - Docente: FABIO ANTONINO BUSACCARisultati di apprendimento attesi
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.
Course Structure
Required Prerequisites
Basics of maths (integrals, derivatives, matrixes, vectors, functions, scientific/exponential notation), basics of communication systems (not strictly required).
Attendance of Lessons
Is not mandatory but is highly recommended
Detailed Course Content
· 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
· 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
· 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)
Textbook Information
Course Planning
| Subjects | Text References | |
|---|---|---|
| 1 | Introduction to Internet of Things. | Rezzani. Big Data Analytics: Il manualedel data scientist, Apogeo Maggioli Editore, Chapter 1 |
| 2 | Introduction to big data -Definition ofbig data - Types of big data -operationson big data -Examples of big data. | Teacher's slides; Chi Yang, DeepakPuthal, Saraju P. Mohanty, and EliasKougianos. Big Sensing Data Curation inCloud Data Center for Next GenerationIoT and WSN, www.smohanty.org |
| 3 | Big data sensing: Types of data-Audiosources - Basics of acoustics- Humanearing fundamentals- Basics of digitalaudio- Digital encoding-Sampling Theory-Different audio file formats-Compressed audio. | V. Lombardo, A. Valle. Audio emultimedia, 4th edition, Apogeo MaggioliEditore, Chapters 1, 2, 3, 4, 6, 8;Teacher's slides; D. Solomon. DataCompression, 4th edition, Springer,Chapters 1, 2, 3 ; D. Solomon. Data |
| 4 | Video sources - Basics of videoencoding-Different video file formats-Multimedia transmission-Fundamentals-Jitter and synchronization-Multimedia fileformats-Data sources-Data file formats-Examples of different mechanisms fordata generation. | Z. Li and M. Drew. Fundamentals of Multimedia, Pearson Chapters 3, 4, 5, 8,9, 10 |
| 5 | Big data compression: Source coding-Compressive sensing-Channel coding.Examples of compression techniquesapplied to different types of data. | Z. Han, H. Li, W. Yin. Compressive sensing for wireless networks. Cambridge University Press Chapters 3, 4, 5, 6;Teacher's slides |
| 6 | Big data communication: Technologiesfor the IoT: LPWAN | U. Raza, P. Kulkarni and M.Sooriyabandara, Low Power Wide AreaNetworks: An Overview, IEEECommunicaXon Surveys and Tutorials,19(2), pp. 855-874, 2017 |
| 7 | Big data communication: Technologiesfor the IoT: LoRa and SigFox | Sigfox Technical Overview, May 2017;Teacher's slides; M. Lavric, V. Popa.Internet of Things and LoRa™ Low-Power Wide-Area Networks: A survey.proc. of 2017 International Symposium on Signals, Circuits and Systems (ISSCS) 2017. |
| 8 | IEEE 802.11 and WiFi | IEEE Standard Recommendations |
Learning Assessment
Learning Assessment Procedures
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