Budapest University of Technology and Economics, Faculty of Electrical Engineering and Informatics

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    IoT - Internet of Things

    A tantárgy neve magyarul / Name of the subject in Hungarian: IoT - Tárgyak internete

    Last updated: 2024. március 4.

    Budapest University of Technology and Economics
    Faculty of Electrical Engineering and Informatics     		MSc, Computer Engineering, specialisation
    Course ID Semester Assessment Credit Tantárgyfélév
    VITMMB13   2/1/0/v 5  
    3. Course coordinator and department Dr. Vidács Attila,
    Web page of the course https://www.tmit.bme.hu/vitmmb13
    4. Instructors
    Dr. Vidács Attila, docens, TMIT
    Dr. Fehér Gábor, docens, TMIT
    Dr. Vida Rolland, docens, TMIT
    5. Required knowledge

    - 

    6. Pre-requisites
    Ajánlott:
    -
    7. Objectives, learning outcomes and obtained knowledge The objective of the course is for students to learn the basics of IoT systems and applications by solving practical problems using real IoT devices.
    8. Synopsis
    LECTURES:
    1. Introduction: the world of IoT, trends in the IoT world. The emergence of IoT in smart cities, smart homes and industry. IoT devices requirements and capabilities.
    2. IoT technologies: Low-power wireless solutions. Resource-efficient solutions. Low-power, low-voltage, low-power solutions.
    3. IoT communication: low-power, short-range radio communication solutions. Low-power, long-range radio communication solutions.
    4. IoT network architectures. Low-power, low-power IoT networks. Measurement data delivery over the Internet. Server/client and advertisement/subscription models in IoT communications. IoT data visualization.
    5. IoT in the cloud: different IoT cloud platforms and their connection to physical sensors. IoT platforms: IBM Watson IoT, Google Cloud, MS Azure, AWS IoT, ThingSpeak, OpenRemote. Communication between different platforms and components.
    6. IoT reliability and security. Authentication of IoT devices.
    7. IoT and Artificial Intelligence. Data processing. Edge AI. Cloud solutions: TorchServe and TensorFlow Serving.
    8. Industrial IoT (IIoT) solutions. IoT and Robotics. IIoT platforms. Case study: EU 5G-SMART, Arrowhead.
    Smart City solutions. Case study: Smart Santander. Massive IoT.
    9. Smart city solutions. Case study: smart Santander. Massive IoT.
    10. Supporting infrastructures for intelligent transport systems. Smart parking solutions.
    11. Smart home. Open source home automation solutions. Home Assistant, OpenHAB.
    12. Environmental monitoring. Case studies: rainforest monitoring, water/air/environmental pollution. Global solutions (e.g. tsunami, earthquake monitoring and forecasting).
    13. eHealth. Wearable devices. WPAN and in-body wireless IoT solutions.
     
    PRACTICES:
     
    1. Detailed presentation, discussion and assignment of the mid-term homework. Transfer of knowledge on safety of devices and students. Presentation of optional tools and platforms.
    2. Sensor communication exercise. Building sensors, connecting to the Internet.
    3. Remote data collection, data visualisation and data analysis. Deployment and use of containerised services.
    4. Homework intermediate checkpoint: presentation of system designs, details of technological solution, joint discussion.
    5. Use and extension of smart home IoT platform.
    6. Use and programming of cloud-based IoT platform.
    7. Presentation, joint discussion and evaluation of homework tasks.
    9. Method of instruction
    - Lectures.
    - Classroom computer exercises.
    - Independent work (homework).
    10. Assessment
    During the semester:

    Writing of 1 summative type midterm exam (ZH) and 1 homework assignment.
    Successful completion of the ZH and the homework is a prerequisite for signing and passing the exam.

    During the examination period:

    Written examination, the mark obtained is determined by the result of the examination paper, the threshold level for a satisfactory mark is 40%.
    11. Recaps
    The ZH can re-taken up once.
    The deadline for the submission of homework is during the semester. The homework may be handed in by the deadline set for the make-up period.
    12. Consultations On request, by prior arrangement with the lecturer.
    13. References, textbooks and resources Online reading material for presentations (book chapters, articles)
    14. Required learning hours and assignment
    Contact hours42
    Mid-term preparation for classes0
    Preparing for mid-term exam (ZH)20
    Homework40
    Mastery of assigned written learning material8
    Exam preparation40
    Total150
    15. Syllabus prepared by Dr. Attila Vidács, associate professor, TMIT