Course details

Principles and Design of IoT

TOI Acad. year 2022/2023 Summer semester 5 credits

The course reflects modern trends in the field of data acquisition and processing from sensors. The lectures provide the foundational knowledge in possibilities of data acquisition from sensors, fusion of data from multiple sensors, themes of data analysis in IoT systems (data mining, classification, decision support algorithms), control of sensor module consumption, communication in IoT systems, design and implementation of IoT systems. In the practical part (project) students will go through all phases of development of simple IoT system from design stage to realization of functional system.


Course coordinator

Language of instruction



Credit+Examination (written)

Time span

  • 26 hrs lectures
  • 8 hrs laboratories
  • 18 hrs projects

Assessment points

  • 55 pts final exam (written part)
  • 15 pts mid-term test (written part)
  • 10 pts labs
  • 20 pts projects




Subject specific learning outcomes and competences

By completing the courses, student gets knowledge about function and composition of IoT system. The acquired knowledge can then be used to implement its own IoT system based on sensor modules, communication means, cloud or actuators. Valuable knowledge can include data processing and analysis for management or decision making purposes.

Learning objectives

In the course, students learn about the possibilities of digitizing physical phenomena of the world, analyzing data from sensors for decision making and with basic concepts of IoT systems. The aim is to teach students the necessary knowledge of IT for design and implementation of IoT systems.

Why is the course taught

In recent years, IoT systems have developed rapidly, therefore systems gradually become an integral part of our lives. From the IT point of view, this is an important area that is in great demand among companies.

Prerequisite knowledge and skills

Valid schooling of Edict No. 50 (work with electrical devices) is needed.

Technical equipment

Rasperry Pi 4, ESP32

Study literature

  • CHOU, Timothy. Precision-Principles, Practices and Solutions for the Internet of Things. McGraw-Hill Education, 2017.
  • ABU-ELKHEIR, Mervat; HAYAJNEH, Mohammad; ALI, Najah. Data management for the internet of things: Design primitives and solution. Sensors, 2013, 13.11: 15582-15612.
  • SAUTER, Martin. From GSM to LTE-advanced Pro and 5G: An introduction to mobile networks and mobile broadband. John Wiley & Sons, 2017.
  • ALIOTO, Massimo (ed.). Enabling the Internet of Things: From Integrated Circuits to Integrated Systems. Springer, 2017.

Fundamental literature

  • SERPANOS, Dimitrios; WOLF, Marilyn. Internet-of-Things (IoT) Systems: Architectures, Algorithms, Methodologies. Springer, 2017.

  • OLENEWA, Jorge. Guide to wireless communications. Cengage Learning, 2013.

  • LEA, Perry. Internet of Things for Architects: Architecting IoT solutions by implementing sensors, communication infrastructure, edge computing, analytics, and security. Packt Publishing Ltd, 2018.

  • DUNNING, Ted; FRIEDMAN, B. Ellen. Time Series Databases: New Ways to Store and Access Data. Sebastopol, CA: O'Reilly Media, 2014.

Syllabus of lectures

  1. Introduction to IoT (What is IoT ?, Summary of available sensors, Communication at sensor data transfer level).
  2. Parts of IoT system (Things, Network, Cloud, Actuators,..).
  3. Communication interfaces used in IoT systems (Unlicensed 2.4 GHz band, Unlicensed 433 MHz and 868 MHz bands, Proprietary NarrowBand technology).
  4. Communication protocols for Internet of Things (Request-Response, Publish-Subscribe, and more).
  5. IoT System Design I.  (Architecture of IoT system).
  6. IoT System Design II. (Consumption of sensor and communication modules, Design of low energy IoT systems).
  7. Time series.
  8. Data management and data analysis in the IoT systems (Data management in centralized and distributed systems, Algorithms for data classification and reduction).
  9. Data visualisation and services (Data structures, Data visualization, IoT support services).
  10. Mobile Technologies for the Internet of Things.
  11. Biometric sensors (Biometric sensors used for authentication in IoT systems, Development of modern sensor systems for biometrics).
  12. Real World Applications of Internet of Things (IoT).
  13. Smart city, Intelligent home.

Syllabus of laboratory exercises

  1. IoT device commissioning.
  2. Multiple sensor data aggregation.
  3. Pattern recognition in time series.
  4. Practical IoT sensor development.

Syllabus - others, projects and individual work of students

  1. Creating a sensor module.
  2. Analysis of data from IoT system.

Progress assessment

  1. Written midterm test
  2. Participation and active work in laboratories + exercises
  3. 2 Projects (get at least 2 points from first project and get at least 3 points from second project)

Controlled instruction

In the case of missed HW laboratories it is possible to replace them until the laboratory is ready for further laboratory practice. Please inform the head of the laboratory or the course supervisor without any delay.

Exam prerequisites

Student must gain at least 15 points during the term. Get at least 2 points from first project and get at least 3 points from second project.


Tue exam 2023-05-23 E104 09:0010:501. oprava
Tue exam 2023-05-30 E105 13:0014:502. oprava
Thu exam 2023-05-11 E104 09:0010:50řádná
Thu laboratory 1., 2. of lectures N104 12:0013:509999 1MIT 2MIT xx
Thu laboratory 5., 9. of lectures L306.1 L306.2 12:0013:509999 1MIT 2MIT xx Goldmann
Thu laboratory 2023-03-30 L306.1 L306.2 12:0013:509999 1MIT 2MIT xx Sakin
Thu laboratory 2023-04-20 L306.1 L306.2 12:0013:509999 1MIT 2MIT xx
Thu other 2023-03-09 L306.1 12:0013:50Laboratorní cvičení 1
Thu other 2023-03-30 L306.1 12:0013:50Laboratorní cvičení 2
Thu other 2023-04-06 L306.1 12:0013:50Laboratorní cvičení 3
Thu other 2023-04-20 L306.1 12:0013:50Laboratorní cvičení 4
Fri lecture 1., 2. of lectures D0207 10:0011:509999 1MIT 2MIT NIDE xx Goldmann
Fri lecture 3., 4., 5. of lectures D0207 10:0011:509999 1MIT 2MIT NIDE xx Vašíček
Fri lecture 7., 8., 10., 11., 12. of lectures D0207 10:0011:509999 1MIT 2MIT NIDE xx
Fri lecture 2023-03-17 N204 10:0011:509999 1MIT 2MIT NIDE xx
Fri lecture 2023-05-05 A112 10:0011:509999 1MIT 2MIT NIDE xx

Course inclusion in study plans

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