AID403 IoT Fundamentals
Syllabus | International University of Sarajevo - Last Update on Feb 02, 2026
Syllabus Quick Jump
Search and navigate to any syllabus instantly
Artificial Intelligence and Data Engineering
Amal Mersni
Course Lecturer
Course Objectives
1. To understand the architecture of IoT solutions, examine the components and relationships within the Internet of Things (IoT), describe the operation of current standards for IoT communication, construct IoT solutions using the Arduino microcontroller or Raspberry Pi single-board computer. 2. To assess the impact of implementation decisions for an IoT system (e.g. choice of communication technology, hardware, interaction model, etc.) 3. To implement and asses an IoT system that can address challenges in home/city automation, logistics, manufacturing, healthcare, or energy systems. 4. To collect and visualize data sourced from IoT endpoints and analyze data for crucial insights, a proficiency that is appreciated by employers.
Learning Outcomes
After successful completion of the course, the student will be able to:
Course Materials
Required Textbook
Cisco Academy Networking Program IoT Fundamentals: Connecting Things, Big Data & Analytics.
Additional Literature
IoT Fundamentals: Networking Technologies, Protocols, and Use Cases for the Internet of Things, David Hanes, Gonzalo Salgueiro, Patrick Grossetete, Robert Barton, Jerome Henry. Cisco Press.Teaching Methods
This course employs a range of teaching and self-learning methods, including Lectures with presentation and notes, in-class examples and discussions, interactive Activities and video demonstrations
Group projects are implemented for hands-on experience, team work and research practice
Weekly Topics
| Week | Topic | Readings / References |
|---|---|---|
| 1 | Introduction to Internet of Things | |
| 2 | IoT Hardware platforms and Operating Systems | |
| 3 | Wireless technologies in IoT - introduction and general overview | |
| 4 | Wireless technologies in IoT - short and long range technologies | |
| 5 | IP-connected smart objects/networks pt1 | |
| 6 | IP-connected smart objects/networks pt2 | |
| 7 | IP-connected smart objects/networks pt3 | |
| 8 | Midterm Exam | |
| 9 | Embedded web services and Web of Things pt1 | |
| 10 | Embedded web services and Web of Things pt2 | |
| 11 | Other relevant standardization bodies, MQTT | |
| 12 | Indoor localization | |
| 13 | IoT Security | |
| 14 | Project demonstrations | |
| 15 | Project demonstrations |
Course Schedule (All Sections)
| Section | Type | Day 1 | Venue 1 | Day 2 | Venue 2 |
|---|---|---|---|---|---|
| AID403.1 | Course | Thursday 17:00 - 19:50 | A F1.4 - Class/Laboratory | - | - |
Office Hours & Room
Assessment Methods and Criteria
Assessment Components
Final Exam
AI: Not AllowedAlignment with Learning Outcomes : 1
Midterm
AI: Not AllowedAlignment with Learning Outcomes : 1
Project
AI: Not AllowedAlignment with Learning Outcomes : 1
Homeworks
AI: Not AllowedAlignment with Learning Outcomes : 1
IUS Grading System
| Grading Scale | IUS Grading System | IUS Coeff. | Letter (B&H) | Numerical (B&H) |
|---|---|---|---|---|
| 0 - 44 | F | 0 | F | 5 |
| 45 - 54 | E | 1 | ||
| 55 - 64 | C | 2 | E | 6 |
| 65 - 69 | C+ | 2.3 | D | 7 |
| 70 -74 | B- | 2.7 | ||
| 75 - 79 | B | 3 | C | 8 |
| 80 - 84 | B+ | 3.3 | ||
| 85 - 94 | A- | 3.7 | B | 9 |
| 95 - 100 | A | 4 | A | 10 |
Late Work Policy
Information about late submission policies will be shared during class and posted in this section. Please check back for official guidelines.
ECTS Credit Calculation
📚 Student Workload
This 6 ECTS credit course corresponds to 150 hours of total student workload, distributed as follows:
Lecture hours
42 hours ⏳ (14 week × 3 h)
Hands on Labs
28 hours ⏳ (14 week × 2 h)
Home study
42 hours ⏳ (14 week × 3 h)
Midterm Exam Study
25 hours ⏳ (5 week × 5 h)
Final exam study
13 hours ⏳ (1 week × 13 h)
150 Total Workload Hours
6 ECTS Credits
Course Policies
Academic Integrity
All work submitted must be your own. Plagiarism, cheating, or any form of academic dishonesty will result in disciplinary action according to university policies. When in doubt about citation practices, consult the instructor.
Attendance Policy
Students are expected to adhere to the attendance requirements as outlined in the International University of Sarajevo Study Rules and Regulations. Excessive absences, whether excused or unexcused, may impact academic performance and eligibility for assessment. Mandatory sessions (e.g., labs, workshops) require attendance unless formally exempted. For detailed policies on absences, documentation, and penalties, please refer to the official university regulations.
Technology & AI Policy
Laptops/tablets may be used for note-taking only during lectures. Phones should be silenced and put away during all class sessions. Audio/video recording requires prior permission from the instructor.
Artificial Intelligence (AI) Usage: The use of AI tools (e.g., ChatGPT, Copilot, Gemini) varies by assessment component. Please refer to the AI usage indicator next to each assessment item in the Assessment Methods and Criteria section above. Submitting AI-generated content as your own work, where AI is not explicitly allowed, constitutes an academic integrity violation.
Communication Policy
All course-related communication should occur through official university channels (institutional email or SIS). Emails should include [AID403] in the subject line.
Academic Quality Assurance Policy
Course Academic Quality Assurance is achieved through Semester Student Survey. At the end of each academic year, the institution of higher education is obliged to evaluate work of the academic staff, or the success of realization of the curricula.
Learning Tips
Be prepared to contribute thoughtfully during class discussions, labs, or collaborative work. Active participation deepens understanding and encourages critical thinking.
Complete assigned readings or prep materials before class. Take notes, highlight key ideas, and jot down questions. Aim to grasp core concepts and their applications—not just facts.
Use course frameworks or methodologies to analyze problems, case studies, or projects. Begin early to allow time for reflection and refinement. Seek feedback to improve your work.
Don’t hesitate to reach out when something is unclear. Use office hours, discussion boards, or peer networks to clarify concepts and stay on track.
Syllabus Last Updated on Feb 02, 2026 | International University of Sarajevo
Print Syllabus
Referencing Curricula Print this page
| Course Code | Course Title | Weekly Hours* | ECTS | Weekly Class Schedule | ||||||
| T | P | |||||||||
| AID403 | IoT Fundamentals | 3 | 2 | 6 | ||||||
| Prerequisite | CS103 | It is a prerequisite to | - | |||||||
| Lecturer | Amal Mersni | Office Hours / Room / Phone | ||||||||
| amersni@ius.edu.ba | ||||||||||
| Assistant | Ismar Aganović | Assistant E-mail | iaganovic@ius.edu.ba | |||||||
| Course Objectives | 1. To understand the architecture of IoT solutions, examine the components and relationships within the Internet of Things (IoT), describe the operation of current standards for IoT communication, construct IoT solutions using the Arduino microcontroller or Raspberry Pi single-board computer. 2. To assess the impact of implementation decisions for an IoT system (e.g. choice of communication technology, hardware, interaction model, etc.) 3. To implement and asses an IoT system that can address challenges in home/city automation, logistics, manufacturing, healthcare, or energy systems. 4. To collect and visualize data sourced from IoT endpoints and analyze data for crucial insights, a proficiency that is appreciated by employers. |
|||||||||
| Textbook | Cisco Academy Networking Program IoT Fundamentals: Connecting Things, Big Data & Analytics. | |||||||||
| Additional Literature |
|
|||||||||
| Learning Outcomes | After successful completion of the course, the student will be able to: | |||||||||
|
||||||||||
| Teaching Methods | This course employs a range of teaching and self-learning methods, including Lectures with presentation and notes, in-class examples and discussions, interactive Activities and video demonstrations. Group projects are implemented for hands-on experience, team work and research practice. | |||||||||
| Teaching Method Delivery | Face-to-face | Teaching Method Delivery Notes | ||||||||
| WEEK | TOPIC | REFERENCE | ||||||||
| Week 1 | Introduction to Internet of Things | |||||||||
| Week 2 | IoT Hardware platforms and Operating Systems | |||||||||
| Week 3 | Wireless technologies in IoT - introduction and general overview | |||||||||
| Week 4 | Wireless technologies in IoT - short and long range technologies | |||||||||
| Week 5 | IP-connected smart objects/networks pt1 | |||||||||
| Week 6 | IP-connected smart objects/networks pt2 | |||||||||
| Week 7 | IP-connected smart objects/networks pt3 | |||||||||
| Week 8 | Midterm Exam | |||||||||
| Week 9 | Embedded web services and Web of Things pt1 | |||||||||
| Week 10 | Embedded web services and Web of Things pt2 | |||||||||
| Week 11 | Other relevant standardization bodies, MQTT | |||||||||
| Week 12 | Indoor localization | |||||||||
| Week 13 | IoT Security | |||||||||
| Week 14 | Project demonstrations | |||||||||
| Week 15 | Project demonstrations | |||||||||
| Assessment Methods and Criteria | Evaluation Tool | Quantity | Weight | Alignment with LOs | AI Usage |
| Final Exam | 1 | 30 | 1 | Not Allowed | |
| Semester Evaluation Components | |||||
| Midterm | 1 | 30 | 1 | Not Allowed | |
| Project | 1 | 20 | 1 | Not Allowed | |
| Homeworks | 2 | 20 | 1 | Not Allowed | |
| *** ECTS Credit Calculation *** | |||||
| Activity | Hours | Weeks | Student Workload Hours | Activity | Hours | Weeks | Student Workload Hours | |||
| Lecture hours | 3 | 14 | 42 | Hands on Labs | 2 | 14 | 28 | |||
| Home study | 3 | 14 | 42 | Midterm Exam Study | 5 | 5 | 25 | |||
| Final exam study | 13 | 1 | 13 | |||||||
| Total Workload Hours = | 150 | |||||||||
| *T= Teaching, P= Practice | ECTS Credit = | 6 | ||||||||
| Course Academic Quality Assurance: Semester Student Survey | Last Update Date: 03/02/2026 | |||||||||