Title:

Principles and Design of IoT

Code:TOI
Ac.Year:2019/2020
Sem:Summer
Curriculums:
ProgrammeField/
Specialization
YearDuty
MITAINADE-Elective
MITAINBIO-Elective
MITAINCPS-Elective
MITAINEMB-Elective
MITAINGRI-Elective
MITAINHPC-Elective
MITAINIDE-Compulsory
MITAINISD-Elective
MITAINISY-Elective
MITAINMAL-Elective
MITAINMAT-Elective
MITAINNET-Elective
MITAINSEC-Elective
MITAINSEN-Elective
MITAINSPE-Elective
MITAINVER-Elective
MITAINVIZ-Elective
Language of Instruction:Czech
Credits:5
Completion:credit+exam (written)
Type of
instruction:
Hour/semLecturesSeminar
Exercises
Laboratory
Exercises
Computer
Exercises
Other
Hours:2608018
 ExamsTestsExercisesLaboratoriesOther
Points:551501020
Guarantor:Drahanský Martin, prof. Ing., Dipl.-Ing., Ph.D. (DITS)
Deputy guarantor:Vašíček Zdeněk, doc. Ing., Ph.D. (DCSY)
Lecturer:Drahanský Martin, prof. Ing., Dipl.-Ing., Ph.D. (DITS)
Vašíček Zdeněk, doc. Ing., Ph.D. (DCSY)
Instructor:Dvořák Michal, Ing. (DITS)
Goldmann Tomáš, Ing. (DITS)
Sakin Martin, Ing. (DITS)
Faculty:Faculty of Information Technology BUT
Department:Department of Intelligent Systems FIT BUT
Schedule:
DayLessonWeekRoomStartEndLect.Gr.Groups
WedlecturelecturesD0206 11:0012:501MIT 2MIT xx
 
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.
Description:
  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.
Knowledge and skills required for the course:
  Valid schooling of Edict No. 50 (work with electrical devices) is needed.
Learning outcomes and competencies:
  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.
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.
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. Data mining in IoT systems.
  4. Biometric Authentication in IoT Systems.
Syllabus - others, projects and individual work of students:
 
  1. Creating a sensor module.
  2. Analysis of data from IoT system.
Fundamental literature:
 
  • 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.
Study literature:
 
  • 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.
  • SERPANOS, Dimitrios; WOLF, Marilyn. Internet-of-Things (IoT) Systems: Architectures, Algorithms, Methodologies. Springer, 2017.
  • OLENEWA, Jorge. Guide to wireless communications. Cengage Learning, 2013.
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.
Progress assessment:
  
  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.
 

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