IoT and Big Data Sensing Compression and Communication

The course aims to provide the basic knowledge and methodological tools required to understand and design the entire pipeline for the generation, acquisition, compression, and transmission of the large volumes of data produced by Internet of Things (IoT) systems. The course will address the definition, characteristics, and taxonomy of Big Data, as well as the structure and formats of data generated in several representative application scenarios, including environmental monitoring, e-health, smart cities, vehicular and maritime environments, together with the fundamental operations performed on such data.

In particular, the course will cover the fundamentals of source coding and lossless and lossy compression techniques applied to sensor data, images, audio, and video. Topics will include entropy coding, transform-based techniques such as the Discrete Cosine Transform (DCT) and wavelet transforms, major multimedia coding standards including JPEG, JPEG2000, MPEG, and H.26x, as well as compressive sensing. The course will also examine technologies, architectures, and access protocols for Big Data communications in IoT environments, including IEEE 802.15.4 and ZigBee, Bluetooth and Bluetooth Low Energy (BLE), 6LoWPAN, IEEE 802.11, LoRa/LoRaWAN, Sigfox, MQTT, and protocols for vehicular networks, such as CAN bus, DSRC, IEEE 802.11p, and related technologies. An introduction to systems based on Software-Defined Radio (SDR) will also be provided.

A specific part of the course will be devoted to sensing and communication scenarios in underwater environments, namely the Internet of Underwater Things (IoUT), as well as intra-body scenarios. In such settings, the constraints imposed by the communication channel make the choice of data compression techniques and communication protocols particularly critical. The course will introduce the theoretical foundations of underwater, intra-body, and molecular communications, and will provide an overview of the current state of the art in research.

During the lectures, students will be introduced to the use of software tools, including MATLAB and network simulation environments, for data analysis, performance evaluation of compression algorithms, and simulation of IoT communication systems.

The course will include practical exercises and a group project aimed at developing hands-on skills, the ability to critically analyze results, and problem-solving abilities. Students will also develop the ability to collaborate and coordinate effectively with their peers, document their work, and publicly present the results achieved. A competition among the groups will also be organized, allowing the most deserving group to obtain a bonus to be added to the final exam grade.

Dublin Descriptors

Knowledge and understanding

Based on the knowledge acquired, the student will be able to understand coding, compression, and multimedia transmission techniques, as well as IoT technologies, access protocols, and distributed communication systems, including the structure of the data generated in terrestrial and underwater big-data sensing scenarios and the constraints affecting their transmission.

Applying knowledge and understanding

At the end of the course, the student will be able to apply compression and coding techniques for multimedia and IoT data, select and dimension the communication technologies best suited to a given big-data sensing scenario, and design, implement, and experimentally evaluate a complete system for data acquisition, compression, and transmission.

Making judgements

At the end of the course, the student will be able to evaluate and interpret experimental laboratory data and critically analyze the performance, costs, and reliability of alternative compression and communication solutions, justifying their design choices. This ability is acquired through participatory exercises and group project activities.

Communication skills

At the end of the course, the student will be able to discuss topics related to sensing, compression, and communication of big data using appropriate technical language, write technical reports and project documentation, and work in groups while publicly presenting the experimental results obtained.

Learning skills

At the end of the course, the student will have acquired the ability to further explore the topics covered independently, consult scientific databases, standards, and bibliographic references, and keep up to date autonomously with the technological evolution of the IoT field. This ability is acquired through the study of scientific articles and the state-of-the-art analysis carried out within the project.

Part 1: Course Introduction (1 hour)
[Lectures: 1 hour – Tutorials and Laboratory: 0 hours]

• Course objectives

• Examination procedures

• Teaching materials

Part 2: Internet of Things and Big Data: Scenarios and Definitions (5 hours)
[Lectures: 5 hours – Tutorials and Laboratory: 0 hours]

• Introduction to the Internet of Things: architectures, enabling technologies, and applications

• Definition, characteristics, and taxonomy of Big Data

• Types of Big Data and Big Data processing operations

• Application scenarios: environmental monitoring, e-health, smart cities, vehicular and maritime environments

Part 3: Big Data Sensing: Data Sources, Types, and Formats (13 hours)
[Lectures: 10 hours – Tutorials and Laboratory: 3 hours]

• Data types and data sources; data generation mechanisms

• Image sources: fundamentals of image coding and image file formats (JPEG, bitmap, and others)

• Video sources: fundamentals of video coding and video file formats

• Multimedia transmission: fundamentals, jitter and synchronization, multimedia container formats (MPEG and H.26x)

Part 4: Big Data Compression (16 hours)
[Lectures: 12 hours – Tutorials and Laboratory: 4 hours]

• Fundamentals of source coding and information theory

• Lossless compression techniques: Huffman coding, Shannon–Fano coding, arithmetic coding, and dictionary-based techniques

• Lossy compression techniques and distortion measures; rate–distortion curves

• Transform-based coding: DCT and wavelet transforms; JPEG and JPEG2000 standards

• Compressive sensing: sparsity, measurement matrices, reconstruction conditions and algorithms

• Examples of compression techniques applied to different types of data

Part 5: Big Data Communication: Technologies and Architectures for IoT (21 hours)
[Lectures: 15 hours – Tutorials and Laboratory: 6 hours]

• Architectures and requirements of communication systems for Big Data

• IEEE 802.15.4, ZigBee, Bluetooth and BLE, 6LoWPAN, MQTT

• IEEE 802.11 and Wi-Fi

• Low-Power Wide-Area Networks (LPWANs): LoRa/LoRaWAN, Sigfox, NB-IoT

• Vehicular networks, DSRC, CAN bus, LIN

• Software-Defined Radio (SDR)

• Examples of node-to-node communication using the technologies covered in the course

Part 6: Sensing and Communication in Unconventional Scenarios (6 hours)
[Lectures: 6 hours – Tutorials and Laboratory: 0 hours]

• Motivation and application scenarios: marine monitoring, surveillance, and offshore infrastructure, intra body

• Underwater communication technologies: acoustic, optical, and magnetic-inductive links; acoustic modems

• The underwater acoustic channel: attenuation, noise, distance-dependent bandwidth, multipath propagation, and Doppler effects

• MAC, routing, and localization protocols for underwater sensor networks

• Underwater data as Big Data: data sources, compression, and in-situ processing

• State of the art in the Internet of Underwater Things: autonomous vehicles, software-defined networks, and machine-learning applications

• Unconventional communication technologies: intra body and molecular

Part 7: Project-Based Activity (17 hours)
[Lectures: 0 hours – Tutorials and Laboratory: 17 hours]

• Specification definition and state-of-the-art analysis

• Design and implementation of a Big Data acquisition, compression, and communication system

• Experimental performance evaluation

• Preparation of technical documentation and presentation of results

• Group-specific projects focusing on two main tracks: terrestrial communication systems based on Software-Defined Radio and sensing and communication networks in unconventional environments

The topics covered in the course and the knowledge acquired are directly or indirectly instrumental to the development of sustainable technological solutions for the efficient acquisition and management of information. They also contribute to high-quality education, in line with Goals 3, 4, 9, 11, 12, 13, and 14 of the 2030 Agenda for Sustainable Development.

Italian books

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

[2] V. Lombardo, A. Valle, Audio e multimedia, 4a edizione, Apogeo Maggioli Editore.

[3] Diapositive proiettate a lezione.

English books

[3] Lecture slides.

[4] D. Salomon, Data Compression: The Complete Reference, 4th edition, Springer.

[5] Z. Li, M. Drew, Fundamentals of Multimedia, Pearson.

[6] Z. Han, H. Li, W. Yin, Compressive Sensing for Wireless Networks, Cambridge University Press.

[7] F. Wu, Advances in Visual Data Compression and Communication: Meeting the Requirements of New Applications, CRC Press.

Scientific papers

[8] U. Raza, P. Kulkarni, M. Sooriyabandara, “Low Power Wide Area Networks: An Overview”, IEEE Communications Surveys and Tutorials, vol. 19, no. 2, pp. 855-874, 2017.

[9] J. Lin, W. Yu, N. Zhang, X. Yang, H. Zhang, W. Zhao, “A Survey on Internet of Things: Architecture, Enabling Technologies, Security and Privacy, and Applications”, IEEE Internet of Things Journal, vol. 4, no. 5, October 2017.

[10] M. Stojanovic, J. Preisig, “Underwater Acoustic Communication Channels: Propagation Models and Statistical Characterization”, IEEE Communications Magazine, vol. 47, no. 1, pp. 84-89, January 2009.

[11] I. F. Akyildiz, D. Pompili, T. Melodia, “Underwater Acoustic Sensor Networks: Research Challenges”, Ad Hoc Networks, vol. 3, no. 3, pp. 257-279, 2005.

[12] N. Jahanbakht, W. Xiang, L. Hanzo, M. R. Azghadi, “Internet of Underwater Things and Big Marine Data Analytics: A Comprehensive Survey”, IEEE Communications Surveys & Tutorials, vol. 23, no. 2, pp. 904-956, 2021.

[13] R. J. Urick, Principles of Underwater Sound, 3rd edition, McGraw-Hill.

[14] Scientific papers and reference standards indicated by the lecturer during the course.

The examination consists of an oral examination and the completion of a group project carried out during the course. The project is compulsory for all students, except for the specific provision for working students described below. At the instructors’ discretion, a written test with open-ended questions may be administered in addition to the oral examination.

The oral examination typically consists of three questions: one on data sources, one on coding and compression techniques, and one on communication technologies and protocols for the IoT. The oral examination typically lasts approximately 30–40 minutes. The final grade, expressed on a scale of 30, is determined by the oral examination and is based on the relevance of the answers, the correctness and completeness of the content, the ability to establish connections with other topics covered in the syllabus, the ability to provide relevant examples, and the appropriate use of technical terminology.

The project concludes with a public presentation, during which the groups present and compare their respective work. The project contributes to the final assessment by awarding one additional point to the examination grade to the members of the group presenting the best project. The evaluation is based on the correctness and originality of the proposed solution, the quality of its implementation and experimental evaluation, and the effectiveness of the presentation. This additional point may also contribute to the award of 30/30 cum laude.

Working students. Working students may request one of the following two options from the instructor within the first three weeks of classes, upon submission of appropriate documentation certifying their status:

• a formal exemption from the attendance requirement, subject to an individual study plan agreed upon with the instructor, including an individual project involving an equivalent level of commitment and intermediate assessment activities, which may also be conducted remotely. This option allows students to obtain the maximum grade of 30/30 cum laude without any restrictions;

• exemption from the project activity, which also removes the attendance requirement. In this case, the examination consists solely of the oral examination, and the maximum grade that can be obtained is 24/30, since, in the absence of the project and its related presentation, the practical and communication skills included among the expected learning outcomes cannot be assessed.

The instructor reserves the right to individually assess, following an interview, cases in which attendance is slightly below the specified thresholds, provided that the circumstances are adequately justified and that the achievement of the expected learning outcomes can nevertheless be ensured.

To ensure equal opportunities and in compliance with current legislation, students concerned may request an individual meeting to plan any appropriate compensatory and/or exemption measures, based on the learning objectives and their specific needs. Students may also contact the CInAP (Centro per l’Integrazione Attiva e Partecipata – Services for Students with Disabilities and/or Specific Learning Disorders) representative of their Department.

Learning assessment may also be conducted remotely, if required by the circumstances and subject to the appropriate approval.

1. Lossless compression techniques and entropy considerations: Huffman and Shannon-Fano coding
   
   
2. Arithmetic coding and dictionary-based methods
   
   
3. Lossy compression, distortion metrics, and rate-distortion trade-offs
   
   
4. Transform coding (DCT and Wavelet transforms)
   
   
5. Image compression (JPEG, JPEG2000) and audio/video standards (MPEG, H.26x)
   
   
6. Compressive sensing: signal sparsity, measurement matrices, and reconstruction algorithms
   
   
7. Short-range IoT protocols: IEEE 802.15.4, ZigBee, Bluetooth, and Bluetooth Low Energy (BLE)
   
   
8. LPWAN technologies (LoRa/LoRaWAN, Sigfox, NB-IoT) and IoT application-layer protocols (MQTT, 6LoWPAN)
   
   
9. IEEE 802.11 (Wi-Fi), vehicular communication protocols (CAN bus, DSRC, IEEE 802.11p), and Software-Defined Radio (SDR)
   
   
10. Underwater acoustic channel characteristics and Internet of Underwater Things (IoUT) architectures