IAZ330 Applied Internet of Things
To view more information for this unit, select Unit Outline from the list below. Please note the teaching period for which the Unit Outline is relevant.
| Unit code: | IAZ330 |
|---|---|
| Prerequisite(s): | IFB102 and IFB104 |
| Equivalent(s): | IAB330 |
| Credit points: | 12 |
| Timetable | Details in HiQ, if available |
| Availabilities |
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| International unit fee | $5,640 |
Unit Outline: Flexible Period - 11A 2026, Non-QUT location, Internal (Start Date: 01 Sep 2026)
| Unit code: | IAZ330 |
|---|---|
| Credit points: | 12 |
| Pre-requisite: | IFB102 and IFB104 |
| Equivalent: | IAB330 |
Overview
This unit equips you with the theoretical foundations and practical expertise needed to design and implement innovative IoT solutions for real-world challenges. You will gain a comprehensive understanding of IoT architectures, sensor integration, data processing workflows, and the application of machine learning in IoT contexts. Through a balanced approach of lectures and hands-on activities, the unit covers critical topics such as IoT system components, hardware-software integration, data collection, preprocessing, storage strategies, and machine learning techniques customised for IoT applications. By emphasising experiential learning, you will develop end-to-end IoT solutions, addressing practical problems and honing skills essential for careers in the dynamic and fast-growing IoT industry.
Learning Outcomes
On successful completion of this unit you will be able to:
- Explain the core principles of IoT architecture, sensor integration, communication protocols, and data processing.
- Apply critical thinking and problem-solving skills to design an IoT solution for a real-world problem, considering social, ethical, privacy, and sustainability impacts.
- Build an IoT solution to address a specific real-world problem, demonstrating functional integration of hardware and software components.
- Implement data processing and machine learning techniques to process and interpret IoT data, extracting actionable insights for decision-making.
- Collaborate professionally with peers in a team environment to complete a project.
- Communicate professionally to specialist audiences in written, visual, and oral formats.
Content
This unit combines theoretical knowledge and hands-on experience to explore IoT solutions.
The theoretical component introduces essential IoT concepts, including sensors and actuators, connectivity, communication protocols, edge computing, cloud infrastructure, data processing, and machine learning.
The practical component emphasises using IoT hardware and software to address real-world challenges. You will learn to interface sensors with IoT node devices, process data at the edge, and integrate IoT devices with cloud services to implement machine learning models for deriving insights from IoT datasets.
Together, these components provide you with a comprehensive understanding of IoT systems, equipping you to design, develop, and deploy solutions that bridge all layers of the 3-tiered (node, edge and cloud) IoT architecture.
Learning Approaches
This unit offers a hands-on approach to understanding key concepts in IoT solution design and implementation by working through these stages in a series of lectures, hands-on labs, case studies, and group projects.
The unit will provide you with the skillset necessary to build innovative IoT solutions using industry-standard tools. The lectures and associated notes will guide you through the critical thinking process, design approach, current and emerging technologies in the IoT space.
Weekly tutorials will introduce you to cutting-edge IoT technologies and critical design considerations. This will give you the opportunity to work closely with tutors and other students to show your comprehension of the theoretical concepts and to investigate IoT sensors and devices, which will then aid you to build your own IoT solutions.
QUT Canvas site will be used for lecture notes, tutorial materials, reading resources, and online class discussions.
Feedback on Learning and Assessment
You can obtain feedback on their progress throughout the unit through the following
mechanisms:
- Ask the teaching staff for advice and assistance during lectures and workshop sessions
- Feedback concerning the project and learning portfolio in the middle of the semester during workshops.
- Private consultation with teaching staff.
Assessment
Overview
The assessment for this unit includes a mix of practical and theoretical components. In Assessment 1, you will complete hands-on activities focused on building, testing, and maintaining IoT systems, with your work documented and submitted via a task sheet during the allocated session. Assessment 2 involves designing and developing a machine learning-driven IoT solution, where you will be assessed on your implementation process, data collection for training, and system evaluation. A final exam will test your understanding of key IoT concepts, architectures, and design principles.
Unit Grading Scheme
7- point scale
Assessment Tasks
Assessment: Practical IoT implementations
This assessment builds on the weekly hands-on activities where you design, test, and maintain IoT systems. In Week 6, you will be given a task similar to those completed in earlier weeks. You will implement your solution and demonstrate it during your Week 6 tutorial, explaining the technical details of how your system works.
Assessment: Design and Develop IoT solution with Data Processing and ML Integration (Project-based)
Assessment 2 – ML-driven IoT Project (Group-Based)
You will be divided into groups of four and tasked with developing a machine learning-driven IoT solution to address a real-world problem, with support provided during tutorial sessions. The project includes:
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2a) Report: A comprehensive document outlining the solution’s functionality and the integration of IoT components, including initial data collection capabilities and the incorporation of ML features.
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2b) Real-world Data Collection: Each student will collect and submit real-world data using their developed IoT systems. Data from all group members will be combined into a shared dataset for training ML models.
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2c) Presentation and Demonstration: A PowerPoint presentation and a live demonstration showcasing the final solution, its ML capabilities, and design approach.
Teamwork Assessment: Self and peer assessments will be completed to evaluate individual contributions and teamwork effectiveness.
Parts 2a and Part 2b of Assessment 2 are eligible for the 48-hour late submission period and assignment extensions.
Assessment: Final Exam
The final exam addresses theoretical and practical material covered during the semester. It will be comprised of multiple-choice questions, short-answer questions for a case study.
Academic Integrity
Academic integrity is a commitment to undertaking academic work and assessment in a manner that is ethical, fair, honest, respectful and accountable.
The Academic Integrity Policy sets out the range of conduct that can be a failure to maintain the standards of academic integrity. This includes, cheating in exams, plagiarism, self-plagiarism, collusion and contract cheating. It also includes providing fraudulent or altered documentation in support of an academic concession application, for example an assignment extension or a deferred exam.
You are encouraged to make use of QUT’s learning support services, resources and tools to assure the academic integrity of your assessment. This includes the use of text matching software that may be available to assist with self-assessing your academic integrity as part of the assessment submission process.
Breaching QUT’s Academic Integrity Policy or engaging in conduct that may defeat or compromise the purpose of assessment can lead to a finding of student misconduct (Code of Conduct – Student) and result in the imposition of penalties under the Management of Student Misconduct Policy, ranging from a grade reduction to exclusion from QUT.
Requirements to Study
Costs
This is a hands-on unit and requires you to purchase the following equipment for project and assessment:
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Microcontroller and Sensor Kit: Includes an Arduino nano 33 IoT microcontroller board and a variety of sensors, available through the QUT Bookshop (approx. $70).
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A Raspberry Pi System-on-Chip (SoC) Computer: models later than 3rd Gen can be used (3A/3B/4/5). Students will be instructed to purchase their own devices from retailers. Previous IFB102 students can reuse their devices. Cost will be $50~$100, depending on the model.
Resources
This unit requires you to purchase certain hardware components listed below and to register for some Cloud Platforms. All other learning materials will be accessible through your Canvas unit site.
Resource Materials
Other
Cloud Platforms: register for free accounts on commercial cloud platforms such as MongoDB Atlas and Particle IO.
A Raspberry Pi System-on-Chip (SoC) Computer: models later than 3rd Gen can be used (3A/3B/4/5). Students will be instructed to purchase their own devices from retailers.Cost will be $50~$100, depending on the model.
Microcontroller and Sensor Kit: Includes an Arduino nano 33 IoT microcontroller board and a variety of sensors (approx. $70).
Risk Assessment Statement
There are no unusual health or safety risks associated with this unit.
Course Learning Outcomes
This unit is designed to support your development of the following course/study area learning outcomes.IZ02 Bachelor of Information Technology
- Demonstrate a broad theoretical and technical knowledge of well-established and emerging IT disciplines, with in-depth knowledge in at least one specialist area aligned to multiple ICT professional roles.
Relates to: ULO1, Final Exam - Critically analyse and conceptualise complex IT challenges and opportunities using modelling, abstraction, ideation and problem-solving to generate, evaluate and justify recommended solutions.
Relates to: ULO2, ULO3, ULO4, Practical IoT implementations, Design and Develop IoT solution with Data Processing and ML Integration (Project-based), Final Exam - Integrate and apply technical knowledge and skills to analyse, design, build, operate and maintain sustainable, secure IT systems using industry-standard tools, technologies, platforms, and processes.
Relates to: ULO2, ULO3, ULO4, Practical IoT implementations, Design and Develop IoT solution with Data Processing and ML Integration (Project-based), Final Exam - Demonstrate initiative, autonomy and personal responsibility for continuous learning, working both independently and collaboratively within multi-disciplinary teams, employing state-of-the-art IT project management methodologies to plan and manage time, resources, and risk.
Relates to: ULO5, Design and Develop IoT solution with Data Processing and ML Integration (Project-based) - Communicate professionally and effectively in written, verbal and visual formats to a diverse range of stakeholders, considering the audience and explaining complex ideas in a simple and understandable manner in a range of IT-related contexts.
Relates to: ULO6, Design and Develop IoT solution with Data Processing and ML Integration (Project-based) - Assess the risks and potential of artificial intelligence (and other disruptive emerging technologies) within an organisation and leverage AI knowledge and skills to solve IT challenges, improve productivity and add value.
Relates to: ULO4, Design and Develop IoT solution with Data Processing and ML Integration (Project-based), Final Exam - Critically reflect, using a human-centric approach, on the social, cultural, ethical, privacy, legal, sustainability, and accessibility issues shaping the development and use of IT, including respecting the perspectives and knowledge systems of Aboriginal and Torres Strait Islander peoples, ensuring IT solutions empower and support people with disabilities, and fostering inclusive and equitable digital technologies that serve diverse communities.
Relates to: ULO2, ULO3, Practical IoT implementations, Design and Develop IoT solution with Data Processing and ML Integration (Project-based), Final Exam