IAB330 Applied IoT and Mobile Technologies
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: | IAB330 |
|---|---|
| Prerequisite(s): | IAB230 or CAB201 or ITD121 |
| Credit points: | 12 |
| Timetable | Details in HiQ, if available |
| Availabilities |
|
| CSP student contribution | $1,118 |
| Domestic tuition unit fee | $4,104 |
| International unit fee | $4,788 |
Unit Outline: Semester 2 2024, Gardens Point, Internal
| Unit code: | IAB330 |
|---|---|
| Credit points: | 12 |
| Pre-requisite: | IAB230 or CAB201 or ITD121 |
| Coordinator: | Darshika Koggalahewa | darshika.koggalahewa@qut.edu.au |
Overview
This unit provides the theoretical knowledge and practical skills to design and develop IoT solutions for real-world applications. Through a blend of theoretical learning and hands-on experience, students will explore the fundamental concepts of IoT architecture, sensor integration, data processing, and machine learning. Students will delve into the architecture and components of IoT systems and gain practical experience with IoT hardware and software with emphasis on data collection, preprocessing, and storage techniques specific to IoT applications, as well as machine learning algorithms tailored for real-world IoT use cases. The unit emphasises practical learning experiences and culminates in the development of IoT solutions for real-world scenarios, preparing students for roles in the rapidly evolving field of IoT.
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.
- Design an appropriate IoT solution to address a specific real-world problem.
- Build the proposed IoT solution as a Minimum Viable Product (MVP) to address a specific real-world problem.
- 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 and communicate professionally in written and oral formats.
Content
This unit covers both theoretical and practical (hands-on) knowledge of IoT solutions.
The theoretical components cover essential concepts of IoT such as sensors, actuators, communication protocols, edge computing, cloud infrastructure as well as data processing and machine learning.
The practical components will focus on using IoT hardware and software to develop IoT solutions for real-world scenarios. Students will learn to interface sensors with IoT platforms, process sensor data at the edge, and integrate IoT devices with cloud services where Machine Learning Models can be implemented for extracting insights from IoT datasets.
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.
Practical and hands-on practical labs will follow that 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
Students 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
Students will be given a problem space to work on throughout the semester. Your participation in weekly tutorials will require understanding the technologies and techniques discussed in lectures and this will be assessed based on the IoT solution designed and developed, whether your work has met predefined criteria. You will develop an AI-Driven IoT solution, in the form of a Minimum Viable Product (MVP). The tutorial sessions will take you through the actual development process, and you are responsible for 1) documenting the MVP’s implementation; 2) collecting real-world data for training the MVP’s AI features and 3) evaluating the MVP’s performance. Your comprehension of IoT concepts and design principles will be thoroughly assessed in the final exam.
Unit Grading Scheme
7- point scale
Assessment Tasks
Assessment: Practical IoT implementations
Students will perform weekly tasks in building, testing, and maintaining IoT systems. These will run until approximately week 6.
This assignment is eligible for the 48-hour late submission period and assignment extensions
Assessment: Design and Develop IoT solution with Data Processing and ML Integration (Project-based)
Project Implementation - you will develop an AI-Driven IoT solution as a Minimum Viable Product (MVP) to address a real-world problem, with guidance provided throughout tutorial sessions. You will use your MVP to collect real-world data and submit the data in the format specified. The data submitted by every student will be pooled together to form a crowd-sourced dataset, which will be used for training the Machine-Learning (ML), with emphasis on evaluating performance metrics to select the most suitable model for the MVP, supported by a justification for the choice. The deliverable includes
2a) a report detailing the MVP's functionalities and the integration of various technologies to achieve them, emphasizing the orchestration of different IoT components. While the MVP will have partial functionality and data-gathering capabilities initially, the AI features will be developed later.
2b) a real-world data using the MVP developed in 2a, contributing to a crowd-sourced dataset for ML model training in Assessment 2c.
2c) A power-point presentation demonstrating the entire AI-Driven IoT solution.
This assessment will require you to undertake a self and peer assessment of the teamwork skills and capabilities of your peers.
This assignment is 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, and a case study.
Academic Integrity
Students are expected to engage in learning and assessment at QUT with honesty, transparency and fairness. Maintaining academic integrity means upholding these principles and demonstrating valuable professional capabilities based on ethical foundations.
Failure to maintain academic integrity can take many forms. It includes cheating in examinations, plagiarism, self-plagiarism, collusion, and submitting an assessment item completed by another person (e.g. contract cheating). It can also include providing your assessment to another entity, such as to a person or website.
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.
Further details of QUT’s approach to academic integrity are outlined in the Academic integrity policy and the Student Code of Conduct. Breaching QUT’s Academic integrity policy is regarded as student misconduct and can lead to the imposition of penalties ranging from a grade reduction to exclusion from QUT.
Resources
Raspberry Pi Kit - we aim to repurpose a significant number of old Raspberry Pi kits owned by FoS and borrow sensors from S909 Lab.
Risk Assessment Statement
There are no unusual health or safety risks associated with this unit.
Standards/Competencies
This unit is designed to support your development of the following standards\competencies.
Engineers Australia Stage 1 Competency Standard for Professional Engineer
1: Knowledge and Skill Base
Relates to: Practical IoT implementations, Design and Develop IoT solution with Data Processing and ML Integration (Project-based)
Relates to: Practical IoT implementations, Design and Develop IoT solution with Data Processing and ML Integration (Project-based)
Relates to: Practical IoT implementations, Design and Develop IoT solution with Data Processing and ML Integration (Project-based), Final Exam
Relates to: Final Exam
2: Engineering Application Ability
Relates to: Practical IoT implementations, Design and Develop IoT solution with Data Processing and ML Integration (Project-based)
Relates to: Practical IoT implementations, Design and Develop IoT solution with Data Processing and ML Integration (Project-based)
Relates to: Practical IoT implementations, Design and Develop IoT solution with Data Processing and ML Integration (Project-based), Final Exam
Relates to: Practical IoT implementations, Design and Develop IoT solution with Data Processing and ML Integration (Project-based), Final Exam
3: Professional and Personal Attributes
Relates to: Final Exam
Relates to: Final Exam
Relates to: Practical IoT implementations, Design and Develop IoT solution with Data Processing and ML Integration (Project-based)
Unit Outline: Semester 2 2024, Online
| Unit code: | IAB330 |
|---|---|
| Credit points: | 12 |
| Pre-requisite: | IAB230 or CAB201 or ITD121 |
Overview
This unit provides the theoretical knowledge and practical skills to design and develop IoT solutions for real-world applications. Through a blend of theoretical learning and hands-on experience, students will explore the fundamental concepts of IoT architecture, sensor integration, data processing, and machine learning. Students will delve into the architecture and components of IoT systems and gain practical experience with IoT hardware and software with emphasis on data collection, preprocessing, and storage techniques specific to IoT applications, as well as machine learning algorithms tailored for real-world IoT use cases. The unit emphasises practical learning experiences and culminates in the development of IoT solutions for real-world scenarios, preparing students for roles in the rapidly evolving field of IoT.
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.
- Design an appropriate IoT solution to address a specific real-world problem.
- Build the proposed IoT solution as a Minimum Viable Product (MVP) to address a specific real-world problem.
- 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 and communicate professionally in written and oral formats.
Content
This unit covers both theoretical and practical (hands-on) knowledge of IoT solutions.
The theoretical components cover essential concepts of IoT such as sensors, actuators, communication protocols, edge computing, cloud infrastructure as well as data processing and machine learning.
The practical components will focus on using IoT hardware and software to develop IoT solutions for real-world scenarios. Students will learn to interface sensors with IoT platforms, process sensor data at the edge, and integrate IoT devices with cloud services where Machine Learning Models can be implemented for extracting insights from IoT datasets.
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.
Practical and hands-on practical labs will follow that 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
Students 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
Students will be given a problem space to work on throughout the semester. Your participation in weekly tutorials will require understanding the technologies and techniques discussed in lectures and this will be assessed based on the IoT solution designed and developed, whether your work has met predefined criteria. You will develop an AI-Driven IoT solution, in the form of a Minimum Viable Product (MVP). The tutorial sessions will take you through the actual development process, and you are responsible for 1) documenting the MVP’s implementation; 2) collecting real-world data for training the MVP’s AI features and 3) evaluating the MVP’s performance. Your comprehension of IoT concepts and design principles will be thoroughly assessed in the final exam.
Unit Grading Scheme
7- point scale
Assessment Tasks
Assessment: Practical IoT implementations
Students will perform weekly tasks in building, testing, and maintaining IoT systems. These will run until approximately week 6.
This assignment is eligible for the 48-hour late submission period and assignment extensions
Assessment: Design and Develop IoT solution with Data Processing and ML Integration (Project-based)
Project Implementation - you will develop an AI-Driven IoT solution as a Minimum Viable Product (MVP) to address a real-world problem, with guidance provided throughout tutorial sessions. You will use your MVP to collect real-world data and submit the data in the format specified. The data submitted by every student will be pooled together to form a crowd-sourced dataset, which will be used for training the Machine-Learning (ML), with emphasis on evaluating performance metrics to select the most suitable model for the MVP, supported by a justification for the choice. The deliverable includes
2a) a report detailing the MVP's functionalities and the integration of various technologies to achieve them, emphasizing the orchestration of different IoT components. While the MVP will have partial functionality and data-gathering capabilities initially, the AI features will be developed later.
2b) a real-world data using the MVP developed in 2a, contributing to a crowd-sourced dataset for ML model training in Assessment 2c.
2c) A power-point presentation demonstrating the entire AI-Driven IoT solution.
This assessment will require you to undertake a self and peer assessment of the teamwork skills and capabilities of your peers.
This assignment is 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, and a case study.
Academic Integrity
Students are expected to engage in learning and assessment at QUT with honesty, transparency and fairness. Maintaining academic integrity means upholding these principles and demonstrating valuable professional capabilities based on ethical foundations.
Failure to maintain academic integrity can take many forms. It includes cheating in examinations, plagiarism, self-plagiarism, collusion, and submitting an assessment item completed by another person (e.g. contract cheating). It can also include providing your assessment to another entity, such as to a person or website.
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.
Further details of QUT’s approach to academic integrity are outlined in the Academic integrity policy and the Student Code of Conduct. Breaching QUT’s Academic integrity policy is regarded as student misconduct and can lead to the imposition of penalties ranging from a grade reduction to exclusion from QUT.
Resources
Raspberry Pi Kit - we aim to repurpose a significant number of old Raspberry Pi kits owned by FoS and borrow sensors from S909 Lab.
Risk Assessment Statement
There are no unusual health or safety risks associated with this unit.
Standards/Competencies
This unit is designed to support your development of the following standards\competencies.
Engineers Australia Stage 1 Competency Standard for Professional Engineer
1: Knowledge and Skill Base
Relates to: Practical IoT implementations, Design and Develop IoT solution with Data Processing and ML Integration (Project-based)
Relates to: Practical IoT implementations, Design and Develop IoT solution with Data Processing and ML Integration (Project-based)
Relates to: Practical IoT implementations, Design and Develop IoT solution with Data Processing and ML Integration (Project-based), Final Exam
Relates to: Final Exam
2: Engineering Application Ability
Relates to: Practical IoT implementations, Design and Develop IoT solution with Data Processing and ML Integration (Project-based)
Relates to: Practical IoT implementations, Design and Develop IoT solution with Data Processing and ML Integration (Project-based)
Relates to: Practical IoT implementations, Design and Develop IoT solution with Data Processing and ML Integration (Project-based), Final Exam
Relates to: Practical IoT implementations, Design and Develop IoT solution with Data Processing and ML Integration (Project-based), Final Exam
3: Professional and Personal Attributes
Relates to: Final Exam
Relates to: Final Exam
Relates to: Practical IoT implementations, Design and Develop IoT solution with Data Processing and ML Integration (Project-based)