Course detail

Principles and Design of IoT

FIT-TOIAcad. year: 2019/2020

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.

Learning outcomes of the course unit

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.

Prerequisites

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

Co-requisites

Not applicable.

Recommended optional programme components

Not applicable.

Recommended or required reading

SERPANOS, Dimitrios; WOLF, Marilyn. Internet-of-Things (IoT) Systems: Architectures, Algorithms, Methodologies. Springer, 2017.
OLENEWA, Jorge. Guide to wireless communications. Cengage Learning, 2013.
PFISTER, Cuno. Getting Started with the Internet of Things: Connecting Sensors and Microcontrollers to the Cloud. " O'Reilly Media, Inc.", 2011.
LEA, Perry. Internet of Things for Architects: Architecting IoT solutions by implementing sensors, communication infrastructure, edge computing, analytics, and security. Packt Publishing Ltd, 2018.
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.
DUNNING, Ted; FRIEDMAN, B. Ellen. Time Series Databases: New Ways to Store and Access Data. Sebastopol, CA: O'Reilly Media, 2014.
SAUTER, Martin. From GSM to LTE-advanced Pro and 5G: An introduction to mobile networks and mobile broadband. John Wiley & Sons, 2017.
HWANG, Kai; CHEN, Min. Big-data analytics for cloud, IoT and cognitive computing. John Wiley & Sons, 2017.
ALIOTO, Massimo (ed.). Enabling the Internet of Things: From Integrated Circuits to Integrated Systems. Springer, 2017.

Planned learning activities and teaching methods

Not applicable.

Assesment methods and criteria linked to learning outcomes

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

Exam prerequisites:
Student must gain at least 15 points during the term. Get at least 3 points from each project.
To successfully pass the course students must earn at least 20 points from the final examination.

Language of instruction

Czech

Work placements

Not applicable.

Aims

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.

Specification of controlled education, way of implementation and compensation for absences

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.

Classification of course in study plans

  • Programme MITAI Master's

    specialization NADE , any year of study, summer semester, 5 credits, optional
    specialization NBIO , any year of study, summer semester, 5 credits, optional
    specialization NGRI , any year of study, summer semester, 5 credits, optional
    specialization NNET , any year of study, summer semester, 5 credits, optional
    specialization NVIZ , any year of study, summer semester, 5 credits, optional
    specialization NCPS , any year of study, summer semester, 5 credits, optional
    specialization NSEC , any year of study, summer semester, 5 credits, optional
    specialization NEMB , any year of study, summer semester, 5 credits, optional
    specialization NHPC , any year of study, summer semester, 5 credits, optional
    specialization NISD , any year of study, summer semester, 5 credits, optional
    specialization NIDE , any year of study, summer semester, 5 credits, compulsory
    specialization NISY , any year of study, summer semester, 5 credits, optional
    specialization NMAL , any year of study, summer semester, 5 credits, optional
    specialization NMAT , any year of study, summer semester, 5 credits, optional
    specialization NSEN , any year of study, summer semester, 5 credits, optional
    specialization NVER , any year of study, summer semester, 5 credits, optional
    specialization NSPE , any year of study, summer semester, 5 credits, optional

Type of course unit

 

Lecture

26 hours, optionally

Teacher / Lecturer

Syllabus

  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.

Laboratory exercise

8 hours, compulsory

Teacher / Lecturer

Syllabus

  1. IoT device commissioning.
  2. Multiple sensor data aggregation.
  3. Data mining in IoT systems.
  4. Biometric Authentication in IoT Systems.

Project

18 hours, compulsory

Teacher / Lecturer

Syllabus

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

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