Manufacturing Data Collection and Analytics

2024/2025

Type: Mago-Lego

Delivered by: Department of Business Informatics

When: 1-3 module

Open to: students of all HSE University campuses

Language: English

ECTS credits: 6

Full Syllabus

Abstract

Manufacturing Data Collection and Analytics is an elective course given in the second year of the master's program. The course introduces Internet of Things field of computer science and hardware implementation as an overview of an industrial environment as a source of data. The course covers two modules and includes physics on electrical schemes and networking, different kinds of the things themselves, various fields of the things implementation, software needed to code the things behaviour and store the data including Internet of Things operating systems and some simple examples of data analytics. During the practice classes students have a lot of assignments based on two hardware plat-forms: Arduino Uno and Raspberry Pi 3/4 with Arduino IDE and Android Studio for Android Things OS (or other Unix/Linux-based OS like Raspbian with its software) respectively. Then students are given a home assignment which replaces the course exam. The home assignment is a hardware-software project based on a simple network of the things with a certain purpose to collect and analyze/react to sensors data (smart home, smart weather station, smart plant, smart lock etc.). The assignment is divided into two parts: the first part is hardware (with systems on module mentioned and various sensors, controls, LEDs etc.) and the second part is software (preferably mobile application) controlling the hardware. This course is practice oriented – much more attention is given to practice, not lectures.

Learning Objectives

getting to know the Internet of Things field including terms, basic concepts and implementations
learning to work with hardware (system on modules and various wires and sensors)
studying coding and programming the hardware
getting skills in mobile application developing to control the hardware
learn how to use, collect, store and analyze the sensors data (machine data collection) with a help of an IoT cloud platform

Expected Learning Outcomes

Be able to understand the main problems of the Big Data Analytics in Industry, get acquainted to the architectural components and programming models used for scalable data analysis
Know the fundamental concepts, principles and approaches to description of the Big Data Landscape in Industry
Learn how to use one of the most common frameworks and tools
basics of Internet of Things functionality, purposes, implementations, applications
how to work with the according hardware
how to use various libraries in the software environment dedicated to Internet of Things
how to code and use the hardware boards to run the code
how to use IoT cloud platforms for collecting and analyzing the sensors data
how to develop a mobile application

Course Contents

Introduction to Internet of Things. IoT Architecture, Examples, Implementations. Gartner Hype Cycle Curve for Emerging Technologies. IoT types.
Sensors, resistors, breadboard, modules, displays and other usual components of an IoT kit. Arduino Uno. Types, connections, ports, modules, electrical circuits, etc.
IoT Operating Systems. Raspberry Pi. Connections, ports, modules, etc. Cloud IoT platforms, data collection and analysis. IoT implementations.
Practice with Arduino Uno and Raspberry Pi. Android Studio and Arduino IDE. Practice with cloud IoT platforms, data collection and analysis. IoT implementations (Smart Home, Smart Factory, Remote Weather Station, Voice Controlled LED Strip etc.).
Building IoT project

Assessment Elements

Homework Assignment
Homework assignment is developing a network of things using one of the system on module boards as a base and the according software and operating system. Passing the assignment means to show the whole system working and to be able to answer any questions regarding the code of the assignment script and theory. Each student gets different assignments which he/she chooses him-/herself.
Laboratory Works
Each student follows the methodical guide on the course practice works and develops 8 hardware + software projects. Passing the ap-plication means to show the whole system working and mobile application controlling it or getting some information from the hardware system and to be able to answer any questions regarding the code of the assignment script and theory.

Interim Assessment

2024/2025 3rd module
0.2 * Homework Assignment + 0.8 * Laboratory Works

Bibliography

Recommended Core Bibliography

AZZOLA, F. (2017). Android Things Projects : Efficiently Build IoT Projects with Android Things. [Place of publication not identified]: Packt Publishing. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=1547024
Nihtianov, S., & Luque, A. (2018). Smart Sensors and MEMS : Intelligent Sensing Devices and Microsystems for Industrial Applications: Vol. Second edition. Woodhead Publishing.
Olof Liberg, Marten Sundberg, Eric Wang, Johan Bergman, Joachim Sachs, & Gustav Wikström. (2020). Cellular Internet of Things : From Massive Deployments to Critical 5G Applications. Academic Press.
Papadopoulos, G., Theoleyre, F., & Vilajosana, X. (2020). Industrial Internet of Things: Specificities and Challenges. https://doi.org/10.1002/ITL2.172
Zoran Gacovski. (2019). Internet of Things. [N.p.]: Arcler Press. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=2013945
Макаров, С. Л. Arduino Uno и Raspberry Pi 3: от схемотехники к интернету вещей : руководство / С. Л. Макаров. — Москва : ДМК Пресс, 2018. — 204 с. — ISBN 978-5-97060-730-5. — Текст : электронный // Лань : электронно-библиотечная система. — URL: https://e.lanbook.com/book/116131 (дата обращения: 00.00.0000). — Режим доступа: для авториз. пользователей.

Recommended Additional Bibliography

9781789538304 - Giacomo Veneri, Antonio Capasso - Hands-On Industrial Internet of Things : create a powerful Industrial IoT infrastructure usingIndustry 4.0 - 2018 - Packt Publishing - https://search.ebscohost.com/login.aspx?direct=true&db=nlebk&AN=1948705 - nlebk - 1948705
Gutschow, E. (2019). Big Data-driven Smart Cities: Computationally Networked Urbanism, Real-Time Decision-Making, and the Cognitive Internet of Things. Geopolitics, History & International Relations, 11(2), 48–54. https://doi.org/10.22381/GHIR11220197
Hassan, Q. F., Khan, A. ur R., & Madani, S. A. (2018). Internet of Things : Challenges, Advances, and Applications. Boca Raton: Chapman and Hall/CRC. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=1663018
Molloy, D. (2016). Exploring Raspberry Pi : Interfacing to the Real World with Embedded Linux. Indianapolis, IN: Wiley. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=1250212
The Utilization of Internet of Things (IoT) for Multi Sensor Data Acquisition using Thingspeak. (2018). Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsbas&AN=edsbas.778289F5

Authors

MAKAROV SERGEY LVOVICH

Course Syllabus