Home/ Majors directory/Cybersecurity/Introduction to the Internet of Things and Big Data Analytics
Introduction to the Internet of Things and Big Data Analytics
Major: Cybersecurity
Code of subject: 6.125.01.E.072
Credits: 4.00
Department: Information Technology Security
Lecturer: Professor Olena Nyemkova
Semester: 5 семестр
Mode of study: денна
Завдання: 1. Know the basic principles of IoT functioning, architecture, protocols, and components.
2. Understand the methods of collecting, storing, and processing large amounts of data in IoT systems.
3. Know modern technologies and tools for Big Data analytics.
4. Be able to develop solutions for integrating IoT with analytical data processing in real-world tasks.
5. Optimize the operation of IoT systems using data analysis and artificial intelligence.
Learning outcomes: KN 10. Use modern software and hardware and evaluate the effectiveness of the quality of decisions.
KN 17. Ability to use the skills of experimental calculations of characteristics and selection of elements of a specific automated system, taking into account the required level of information protection in the organization (enterprise).
KN 22. Knowledge of basic models of vulnerabilities, threats and attacks to justify options for building an automated information security monitoring system for information and communication systems and its main components.
KN 1.3. Provide processes of protection and functioning of information and telecommunication (automated) systems based on practices, skills and knowledge of structural (structural-logical) schemes, network topology, modern architectures and models of protection of electronic information resources with reflection of interconnections and information flows, processes for internal and remote components.
KN 1.4. Apply theories and methods of protection to ensure the security of information in information and telecommunications systems.
KN 1.6. Solve problems of protection of information processed in information and telecommunication systems using modern methods and means of cryptographic protection of information.
Required prior and related subjects: Computer networks
Database protection
Blockchain technology
Summary of the subject: The course forms an idea of the main trends in the field of security of the Internet of Things and Big Data Analytics.
Provides the ability to analyze threats to Internet of Things systems based on the attacker's model. Provides knowledge of the classification of vulnerabilities in the "Internet of Things" according to OWASP and IoT Attack Surface Areas Project.
Provides the ability to independently apply the principles of security in the Internet of Things: at the level of devices and gateways, network and transport protocols, applications, as well as independently apply cryptographic mechanisms and protocols adapted for devices with limited computing power.
Опис: Topic 1. Basic principles of IoT functioning: architecture, protocols, and components
Description: Introduction to the concept of the Internet of Things (IoT), basic principles of IoT systems. Consideration of IoT architecture (device, network, data processing levels), data transfer protocols (MQTT, CoAP, HTTP, WebSocket) and key components (sensors, actuators, microcontrollers, gateways).
Topic 2. Collection, storage, and processing of large volumes of data in IoT systems
Description: Methods and technologies for collecting data from IoT devices. Features of working with streaming data in real-time. Overview of data warehouses (SQL, NoSQL, Time-Series DB) and platforms for processing big data (Hadoop, Apache Spark, Kafka).
Topic 3. Modern technologies and tools for big data analytics (Big Data)
Description: Overview of the main tools and methodologies for big data analytics. Using cloud platforms (AWS, Azure, Google BigQuery) and specialized libraries (Pandas, NumPy, TensorFlow) for data processing and analysis, building forecasting models.
Topic 4. Developing solutions for integrating IoT with analytical data processing in real-world tasks
Description: Creating practical IoT projects using analytics. Developing software and hardware solutions for collecting and processing data in industries such as smart homes, production automation, and environmental monitoring.
Topic 5. Optimizing IoT systems using data analysis and artificial intelligence
Description: Using machine learning and artificial intelligence methods to improve the efficiency of IoT systems. Developing models for forecasting, classification, and anomaly detection. Integrating intelligent algorithms into IoT solutions for process automation and real-time decision-making.
Assessment methods and criteria: The following methods are used to diagnose knowledge: oral individual interview at each laboratory lesson, individual defense of laboratory reports; credit test at the end of the semester. The maximum score in points: 100, in particular: Execution and defense of laboratory work: 70, exam control: 30.
Критерії оцінювання результатів навчання: Assessment of student learning outcomes is based on various activities that cover theoretical knowledge and practical skills. The assessment criteria take into account the depth of understanding of the material, the ability to apply knowledge in practice, as well as the quality of individual and group tasks.
Main criteria
1. Knowledge of theoretical foundations.
Demonstration of understanding of the basic principles of IoT systems, architecture, protocols, and components.
Clear explanation of methods for collecting, storing, and processing data.
Knowledge of modern tools for big data analytics.
Assessment: tests, oral answers, essays (20% of the total score).
2. Practical skills.
Ability to develop IoT solutions for data collection and processing.
Ability to use analytical tools to work with big data.
Building models and systems that integrate IoT with analysis methods.
Assessment: laboratory work, practical tasks (30% of the total score).
3. Individual and group projects.
Development and presentation of an IoT solution using big data for a real-world problem (smart home, environmental monitoring, etc.).
Quality of technical implementation, creativity of the approach, and report design.
Assessment: project assessment, presentation, report (30% of the total score).
4. Activity and independence in learning.
Participation in discussions, completion of additional tasks, and independent work on studying new tools and technologies.
Assessment: activity in classes, independent tasks (10% of the total score).
5. Final control.
Completion of a comprehensive task or test covering all topics of the discipline.
Assessment: final exam or test (10% of the total score).
Порядок та критерії виставляння балів та оцінок: 100–88 points – (“excellent”) is awarded for a high level of knowledge (some inaccuracies are allowed) of the educational material of the component contained in the main and additional recommended literary sources, the ability to analyze the phenomena being studied, in their interrelation and development, to answer the questions posed clearly, concisely, logically, consistently, the ability to apply theoretical provisions when solving practical problems; 87–71 points – (“good”) is awarded for a generally correct understanding of the educational material of the component, including calculations, reasoned answers to the questions posed, which, however, contain certain (insignificant) shortcomings, for the ability to apply theoretical provisions when solving practical problems; 70–50 points – (“satisfactory”) is awarded for weak knowledge of the educational material of the component, inaccurate or poorly reasoned answers, with a violation of the sequence of presentation, for weak application of theoretical provisions when solving practical problems; 49–26 points – (“not certified” with the possibility of retaking the semester test) is given for ignorance of a significant part of the educational material of the component, significant errors in answering questions, inability to apply theoretical provisions when solving practical problems; 25–00 points – (“unsatisfactory” with mandatory re-study) is given for ignorance of a significant part of the educational material of the component, significant errors in answering questions, inability to navigate when solving practical problems, ignorance of basic fundamental provisions.
Recommended books: 1. Internet of Things for Industry and Human Applications. Volume 1. Fundamentals and Technologies. Edited by V. S. Kharchenko. National Aerospace University "Kharkiv Aviation Institute". 2019. - 608 p.
2. A.V. Parkhomenko, OM Gladkova, JI Zalyubovsky, Engineering of embedded systems: Textbook. Zaporozhye: Wild Field, 2017.
3. Arduino development environment. [Electronic resource]. - Access mode: http://www.arduino.cc/en/Main/Software
4. Sommer U. Programming of microcontroller boards Arduino / Freeduino.- SPb .: BHV - St. Petersburg, 2012.- 256 p.
Уніфікований додаток: Lviv Polytechnic National University ensures the realization of the right of persons with disabilities to obtain higher education. Inclusive educational services are provided by the Service of Accessibility to Learning Opportunities "Without Limits", the purpose of which is to ensure constant individual support of the educational process of students with disabilities and chronic diseases. An important tool for implementing inclusive educational policy at the University is the Program for Advanced Training of Scientific and Pedagogical Employees and Teaching and Support Staff in the Field of Social Inclusion and Inclusive Education. Contact the address:
2/4 Karpinskogo St., I-th, room 112
E-mail: nolimits@lpnu.ua
Websites: https://lpnu.ua/nolimits https://lpnu.ua/integration
Академічна доброчесність: The policy on the academic integrity of participants in the educational process is formed based on adherence to the principles of academic integrity, taking into account the norms of the "Regulations on Academic Integrity at the National University "Lviv Polytechnic"" (approved by the Academic Council of the University on June 20, 2017, Protocol No. 35).
Introduction to the Internet of Things and Big Data Analytics (курсова робота)
Major: Cybersecurity
Code of subject: 6.125.01.E.073
Credits: 2.00
Department: Information Technology Security
Lecturer: Professor Olena Nyemkova
Semester: 5 семестр
Mode of study: денна
Завдання: 1. Know the basic principles of IoT functioning, architecture, protocols, and components.
2. Understand the methods of collecting, storing, and processing large amounts of data in IoT systems.
3. Know modern technologies and tools for Big Data analytics.
4. Be able to develop solutions for integrating IoT with analytical data processing in real-world tasks.
5. Optimize the operation of IoT systems using data analysis and artificial intelligence.
Learning outcomes: KN 10. Use modern software and hardware and evaluate the effectiveness of the quality of decisions.
KN 17. Ability to use the skills of experimental calculations of characteristics and selection of elements of a specific automated system, taking into account the required level of information protection in the organization (enterprise).
KN 22. Knowledge of basic models of vulnerabilities, threats and attacks to justify options for building an automated information security monitoring system for information and communication systems and its main components.
KN 1.3. Provide processes of protection and functioning of information and telecommunication (automated) systems based on practices, skills and knowledge of structural (structural-logical) schemes, network topology, modern architectures and models of protection of electronic information resources with reflection of interconnections and information flows, processes for internal and remote components.
KN 1.4. Apply theories and methods of protection to ensure the security of information in information and telecommunications systems.
KN 1.6. Solve problems of protection of information processed in information and telecommunication systems using modern methods and means of cryptographic protection of information.
Required prior and related subjects: Computer networks
Database protection
Blockchain technology
Summary of the subject: The course forms an idea of the main trends in the field of security of the Internet of Things and Big Data Analytics.
Provides the ability to analyze threats to Internet of Things systems based on the attacker's model. Provides knowledge of the classification of vulnerabilities in the "Internet of Things" according to OWASP and IoT Attack Surface Areas Project.
Provides the ability to independently apply the principles of security in the Internet of Things: at the level of devices and gateways, network and transport protocols, applications, as well as independently apply cryptographic mechanisms and protocols adapted for devices with limited computing power.
Опис: Topic 1. Basic principles of IoT functioning: architecture, protocols, and components
Description: Introduction to the concept of the Internet of Things (IoT), basic principles of IoT systems. Consideration of IoT architecture (device, network, data processing levels), data transfer protocols (MQTT, CoAP, HTTP, WebSocket) and key components (sensors, actuators, microcontrollers, gateways).
Topic 2. Collection, storage, and processing of large volumes of data in IoT systems
Description: Methods and technologies for collecting data from IoT devices. Features of working with streaming data in real-time. Overview of data warehouses (SQL, NoSQL, Time-Series DB) and platforms for processing big data (Hadoop, Apache Spark, Kafka).
Topic 3. Modern technologies and tools for big data analytics (Big Data)
Description: Overview of the main tools and methodologies for big data analytics. Using cloud platforms (AWS, Azure, Google BigQuery) and specialized libraries (Pandas, NumPy, TensorFlow) for data processing and analysis, building forecasting models.
Topic 4. Developing solutions for integrating IoT with analytical data processing in real-world tasks
Description: Creating practical IoT projects using analytics. Developing software and hardware solutions for collecting and processing data in industries such as smart homes, production automation, and environmental monitoring.
Topic 5. Optimizing IoT systems using data analysis and artificial intelligence
Description: Using machine learning and artificial intelligence methods to improve the efficiency of IoT systems. Developing models for forecasting, classification, and anomaly detection. Integrating intelligent algorithms into IoT solutions for process automation and real-time decision-making.
Assessment methods and criteria: The following methods are used to diagnose knowledge: oral individual interview at each laboratory lesson, individual defense of laboratory reports; credit test at the end of the semester. The maximum score in points: 100, in particular: Execution and defense of laboratory work: 70, exam control: 30.
Критерії оцінювання результатів навчання: Assessment of student learning outcomes is based on various activities that cover theoretical knowledge and practical skills. The assessment criteria take into account the depth of understanding of the material, the ability to apply knowledge in practice, as well as the quality of individual and group tasks.
Main criteria
1. Knowledge of theoretical foundations.
Demonstration of understanding of the basic principles of IoT systems, architecture, protocols, and components.
Clear explanation of methods for collecting, storing, and processing data.
Knowledge of modern tools for big data analytics.
Assessment: tests, oral answers, essays (20% of the total score).
2. Practical skills.
Ability to develop IoT solutions for data collection and processing.
Ability to use analytical tools to work with big data.
Building models and systems that integrate IoT with analysis methods.
Assessment: laboratory work, practical tasks (30% of the total score).
3. Individual and group projects.
Development and presentation of an IoT solution using big data for a real-world problem (smart home, environmental monitoring, etc.).
Quality of technical implementation, creativity of the approach, and report design.
Assessment: project assessment, presentation, report (30% of the total score).
4. Activity and independence in learning.
Participation in discussions, completion of additional tasks, and independent work on studying new tools and technologies.
Assessment: activity in classes, independent tasks (10% of the total score).
5. Final control.
Completion of a comprehensive task or test covering all topics of the discipline.
Assessment: final exam or test (10% of the total score).
Порядок та критерії виставляння балів та оцінок: 100–88 points – (“excellent”) is awarded for a high level of knowledge (some inaccuracies are allowed) of the educational material of the component contained in the main and additional recommended literary sources, the ability to analyze the phenomena being studied, in their interrelation and development, to answer the questions posed clearly, concisely, logically, consistently, the ability to apply theoretical provisions when solving practical problems; 87–71 points – (“good”) is awarded for a generally correct understanding of the educational material of the component, including calculations, reasoned answers to the questions posed, which, however, contain certain (insignificant) shortcomings, for the ability to apply theoretical provisions when solving practical problems; 70–50 points – (“satisfactory”) is awarded for weak knowledge of the educational material of the component, inaccurate or poorly reasoned answers, with a violation of the sequence of presentation, for weak application of theoretical provisions when solving practical problems; 49–26 points – (“not certified” with the possibility of retaking the semester test) is given for ignorance of a significant part of the educational material of the component, significant errors in answering questions, inability to apply theoretical provisions when solving practical problems; 25–00 points – (“unsatisfactory” with mandatory re-study) is given for ignorance of a significant part of the educational material of the component, significant errors in answering questions, inability to navigate when solving practical problems, ignorance of basic fundamental provisions.
Recommended books: 1. Internet of Things for Industry and Human Applications. Volume 1. Fundamentals and Technologies. Edited by V. S. Kharchenko. National Aerospace University "Kharkiv Aviation Institute". 2019. - 608 p.
2. A.V. Parkhomenko, OM Gladkova, JI Zalyubovsky, Engineering of embedded systems: Textbook. Zaporozhye: Wild Field, 2017.
3. Arduino development environment. [Electronic resource]. - Access mode: http://www.arduino.cc/en/Main/Software
4. Sommer U. Programming of microcontroller boards Arduino / Freeduino.- SPb .: BHV - St. Petersburg, 2012.- 256 p.
Уніфікований додаток: Lviv Polytechnic National University ensures the realization of the right of persons with disabilities to obtain higher education. Inclusive educational services are provided by the Service of Accessibility to Learning Opportunities "Without Limits", the purpose of which is to ensure constant individual support of the educational process of students with disabilities and chronic diseases. An important tool for implementing inclusive educational policy at the University is the Program for Advanced Training of Scientific and Pedagogical Employees and Teaching and Support Staff in the Field of Social Inclusion and Inclusive Education. Contact the address:
2/4 Karpinskogo St., I-th, room 112
E-mail: nolimits@lpnu.ua
Websites: https://lpnu.ua/nolimits https://lpnu.ua/integration
Академічна доброчесність: The policy on the academic integrity of participants in the educational process is formed based on adherence to the principles of academic integrity, taking into account the norms of the "Regulations on Academic Integrity at the National University "Lviv Polytechnic"" (approved by the Academic Council of the University on June 20, 2017, Protocol No. 35).