| Course Code | COMP6733 |
| Course Title |
IoT Design Studio
|
| Units of Credit | 6 |
| Lecturer | Wen Hu |
| Admin | Wen Hu |
| Classes |
Lectures: Mon 14:00-16:00 Hrs (Ainsworth 102), Thu 14:00 -16:00 Hrs (CivEng G1) (
Campus Map
)
Timetable for all classes. |
| Consultations |
Monday: 13:00 -13:55
Venue: Room 606, K-17 |
| Course Website |
https://webcms3.cse.unsw.edu.au/COMP6733/23T2/
|
| Course Contact Email | cs6733@cse.unsw.edu.au |
| Handbook Entry |
https://www.handbook.unsw.edu.au/undergraduate/courses/current/COMP6733
|
The aim of the lectures is to facilitate learning and understanding of the important concepts within the course syllabus. Lecture notes will be available at the course web site for downloading before the lecture. In addition to attending the lectures ( mandatory ), students are also asked to study the recommended reading materials before and after the lecture. A number of study problems will be issued each week. These problems give the students a chance to test whether they have understood concepts that are introduced in the lecture. Time in the lecture, if available, may be used to discuss some of these study problems. Solutions to these problems will be available on the course web site.
This course will also provide practical training on programming Intenet of Things (IoT) devices. Each student will be loaned at least one IoT device during the duration of this course for them to work on the laboratory exercises and project.
Guided laboratory on IoT programming will take place from Weeks 2-6 and 7 for 4 hours per week. Laboratory worksheet in self-study style will be issued on the course web site. A laboratory demonstrator will be available to help students with their work. Each laboratory exercise comes with an assessment exercise. Students are expected to demonstrate the assessment to their laboratory demonstrators in the next lab session in order to receive the allotted marks for the exercise.
The aim of project is to give students an opportunity to work on an extended development IoT project. The project work will be done in teams of up to 4 students.
There will be 4 hours of lectures every week:
(i) 2-hour lecture on Monday 14:00 - 16:00 (Ainsworth 202) and
(ii) 2-hour lecture on Thursday 14:00 - 16:00 (CivEng G1)
There will be 4-hour labs in Weeks 2-5 and 7. The detailed lab schedule will be posted on The detailed course timetable is available
here
Students will learn the fundamental principles behind designing IoT.
Topics include a selection from: IoT technology and services, IoT system architecture, Low Power communications (Bluetooth Low Energy and 6LoWPAN) and security issues, time synchronisation and localisation, sensor data smoothing and filtering, light-weight machine learning and data fusion, inertial sensing, activity recognition, biometric authentication and cloud services.
This course contributes to the development of the following graduate capabilities:
|
Graduate Capability |
Acquired in |
|
Scholarship: of their discipline in its interdisciplinary context |
Lectures, labs, problem set |
|
Scholarship: Capable of independent and collaborative |
Labs, problem set |
|
Scholarship: rigorous in their analysis, critique, and reflection |
Lectures, labs, problem set, project |
|
Scholarship: able to apply their knowledge and skills to solving problems |
Labs, problem set, project |
|
Scholarship: capable of effective communication |
Lectures, labs, project |
|
Scholarship: digitally literate |
All aspects of the course |
|
Scholarship: information literate |
All aspects of the course |
|
Leadership: collaborative team workers |
Labs, project |
|
Professionalism: capable of independent, self-directed practice |
All aspects of the course |
|
Professionalism: capable of lifelong learning |
All aspects of the course |
|
Professionalism: capable of operating within an agreed Code of Practice |
Labs, assignment, project |
|
Global citizens: culturally aware and capable of respecting diversity and acting in socially /responsible ways |
Labs, course forums |
Pre-requisites / Co-requisites
The course consists of the following learning components:
The aim of the lectures is to facilitate learning and understanding of the important concepts within the course syllabus. Lecture notes will be available at the course web site for downloading before the lecture. In addition to attending the lectures ( mandatory ), students are also asked to study the recommended reading materials before and after the lecture. A number of study problems will be issued each week. These problems give the students a chance to test whether they have understood concepts that are introduced in the lecture. Time in the lecture, if available, may be used to discuss some of these study problems. Solutions to these problems will be available on the course web site.
This course will also provide practical training on programming Intenet of Things (IoT) devices. Each student will be loaned at least one to two sensortags during the duration of this course for them to work on the laboratory exercises and project.
Guided laboratory on IoT programming will take place in Weeks 2-5, and 7 for 4 hours per week. Laboratory worksheet in self-study style will be issued on the course web site. A laboratory demonstrator will be available to help students with their work. Each laboratory exercise comes with an assessment exercise. Students are expected to demonstrate the assessment to their laboratory demonstrators in the next lab session in order to receive the allotted marks for the exercise.
The aim of project is to give students an opportunity to work on an extended development IoT project. The project work will be done in teams of up to 3 students.
There will be four assessment components as listed below:
| Component | Weight |
|---|---|
| Lab Exercises | 30% |
| Problem Set | 10% |
| Project plan and preliminary founding class presentation | 10% |
| Project milestone class presentation | 10% |
| Project final report (15%) and demo (25%) | 40% |
The following assessment rules apply:
You must attempt all assessment components. Your raw score is computed as the weighted sum (with the weightings listed above) of the score for each assessment component. Your final score will be computed according to the following rules: If you obtain 40% or more for all three assessment components, your final score equals to your raw score. If you obtain less than 40% for any one of the three assessment components, your final score is the smaller of your raw score and 64. For example, If at least one of your assessment components is less than 40% and your raw score is 70, your final score will be 64. If at least one of your assessment components is less than 40% and your raw score is 50, your final score will be 50.
Late submission of any form of assessments:
Assessments submitted late are subject to the following penalty: the obtainable mark reduces by 5 % per day late. Assessments handed in over 5 days (120 hours) late will receive no marks.
Plagiarism is defined as using the words or ideas of others and presenting them as your own UNSW and CSE treat plagiarism as academic misconduct, which means that it carries penalties as severe as being excluded from further study at UNSW. There are several sources to help you understand what plagiarism is and how it is dealt with at UNSW:
Make sure that you read and understand these. Ignorance is not accepted as an excuse for plagiarism. In particular, you are responsible for the of your assignment files such that they are not accessible by anyone but you by setting proper permissions on your CSE home directory and/or on online code repositories. Note also that plagiarism includes paying or asking another person to do a piece of work for you and then submitting it as your own work.
Use of Generative Tools in COMP6733
Systems such as Github Copilot and ChatGPT based on large language models or other generative artificial intelligence techniques, look likely to become heavily used by programmers. However, you need a good understanding of the language you are coding in and the systems involved before you can effectively use these tools. Using these tools to generate code for COMP6733 instead of writing the code yourself will hinder your learning.
You are NOT permitted to submit code generated by automatic tools such as Github Copilot, ChatGPT, Google Bard in COMP6733 including for lab exercises, assignments and weekly tests. Submitting code generated by Github Copilot, ChatGPT, Google Bard and similar tools will be treated as plagiarism.
The following table lists the
tentative
weekly schedule. Students will be informed of any changes during the lecture and by announcements on the notices page.
| Week | Lecture Dates | Lecturer | Lecture Topics | Labs | Remarks |
|---|---|---|---|---|---|
| 1 |
29 May & 1 June
2023 |
Wen |
|
|
|
| 2 | 5 & 8 June 2023 | Wen |
|
Getting started and Arduino fundamentals
(Assessment to be demonstrated in the next lab)
|
|
| 3 | 12 & 15 June 2023 |
Wen,
|
|
Networking
(Assessment to be demonstrated in the next lab)
|
Project teams and topics finalised
|
| 4 | 19 & 22 June 2023 |
Wen,
|
|
Ranging
(Assessment to be demonstrated in the next lab) |
|
| 5 |
26 & 29 June 2023
|
Wen |
|
(Assessment to be demonstrated in Week 7)
|
Project Plan Due
|
| 6 |
3 & 6 July 2023
|
|
|
|
|
| 7 | 10 & 13 July 2023 |
Wen
|
|
Machine Learning
|
|
|
8
|
17 & 20 July 2023
|
Wen |
|
|
Project Progress Report Due
|
| 9 | 24 & 27 July 2023 |
|
|
|
Problem Set Due
|
| 10 |
31 July & 3 Aug
2023 |
Wen
|
|
|
Final Project Report due on 17:00, 6 Aug, Sunday. |
| 11 |
7 Aug 2023
|
Wen |
Project demo and interview
|
|
Return all equipment at the end of project demo and interview.
|
IoT is a young, emerging and rapdily evolving field. Thus, the course materials will draw from books, professional journals and conference papers.
You will find the following books in the library to be of interest:
They are also available at UNSW bookstore:
We will be using a number of journal or conference papers in this course. These will be provided on the course web site and you can access them using your zpass.
Guided laboratory and laboratory assessment:
Students are required to attend a guided laboratory from Weeks 2-5, and 7 where they will learn programming IoT devices. Laboratory worksheets will be available on the course website. There will be a lab demonstrator assisting in the laboratory.
The equipment to be used is the Arduino Nano. Each student will be loaned one Arduino Nano for doing the laboratory and the project. The students will need to sign a loan form and undertake to return these motes in good condition. The motes will be handed out in the laboratory session in the first lab. You must return them at the end of your project demo and interview.
The first lab will provide a basic overview of Arduino Nano and its programming environments (e.g., micro python) . Each lab has an assessment exercise that comes with it. You are given a week to complete these exercises and you will need to demonstrate this to the lab demonstrator in the next laboratory session. Although there is no laboratory exercise for Week 7, you will still need to attend the laboratory to do the demonstration. Apart from the demonstration, you are NOT required to attend the full lab sessions.
Projects:
The aim of the project is to give the students an opportunity to do a small research and development project in IoT. The project work is to be performed in teams of up to 3 students.
The modus operandi is:
Note that there is no formal examination for COMP6733.
Student feedback on this course, and on the effectiveness of lectures in this course, is obtained via electronic survey at the end of each term. Student feedback is taken seriously. Continual improvements are made to the course based in part on this feedback, and will be discussed in the Week 1's lecture . Students are strongly encouraged to let the lecturer in charge know of any problems as soon as they arise. Suggestions and criticisms will be listened to openly, and every action will be taken to correct any issue or improve the students’ learning experience.
Feedback from pervious years indicates that students would prefer to use Python as programming language. We will address this by changing the lab equipment from Sensortag to Arduino Nano BLE sense, which supports micropython programming environment.
You can view the Special Consideration policy at the link here
UNSW handles special centrally (in the Student Lifecycle division), so all special must be submitted via the UNSW Special Consideration website. If your work in this course is affected by unforeseen adverse circumstances, you should apply for Special Consideration. Special must be accompanied by documentation on how you have been affected, which will be verified by Student Lifecycle. Do not email the course directly about special consideration. If your request is reasonable and your work has clearly been impacted, then
Note the use of the word "may". None of the above is guaranteed. It depends on you making a convincing case that the circumstances have clearly impacted your ability to work. Note that UNSW expects you to be available to sit Supplementary Exams, if required. If you are awarded a supplementary exam and do not attend, then your exam mark will be zero.
If you are registered with Disability Services, please forward your documentation to your Lecturer within the first two weeks of term.
Contacting LiC and Course Admin: No personal emails please.
Resource created Tuesday 02 May 2023, 08:21:51 AM, last modified Thursday 01 June 2023, 04:34:45 PM.