COMP5047: Semester 2, 2022

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Units COMP5047 Semester 2 2022 [Normal day]

Unit outline_

COMP5047: Pervasive Computing

Semester 2, 2022 [Normal day] - Camperdown/Darlington, Sydney

Overview

This is an advanced course on Pervasive Computing, with a focus on the Internet of Things (IoT). It introduces the key aspects of the IoT and explores these in terms of the new research towards creating user interfaces that disappear into the environment and are available pervasively, for example in homes, workplaces, cars and carried.

Unit details and rules

Academic unit	Computer Science
Credit points	6
Prerequisites ?	None
Corequisites ?	None
Prohibitions ?	None
Assumed knowledge ?	ELEC1601 and (COMP2129 or COMP2017 or COMP9017). Background in programming and operating systems that is sufficient for the student to independently learn new programming tools from standard online technical materials
Available to study abroad and exchange students	Yes

Teaching staff

Coordinator	Anusha Withana, anusha.withana@sydney.edu.au

Type	Description	Weight	Due	Length
Final exam (Open book)	Final exam Final exam (online)	50%	Formal exam period	2 hours
Outcomes assessed:
Assignment	Quizzes This is a short, in-class quiz (10-15mins) in Week 04 to Week 13	15%	Multiple weeks	N/A
Outcomes assessed:
Assignment	Project Group assignment for projects as a report, video and a presentation	35%	Multiple weeks	n/a
Outcomes assessed:
= hurdle task ? = group assignment ? = Type C final exam ?

Assessment summary

Project: Students are to to create a prototype pervasive computing system, evaluate it and report on the approaches used for the design and evaluation. The assessment will be based on two reports, a video and a presentation.

Detailed information for each assessment can be found on Canvas.

Assessment criteria

The University awards common result grades, set out in the Coursework Policy 2014 (Schedule 1).

As a general guide, a high distinction indicates work of an exceptional standard, a distinction a very high standard, a credit a good standard, and a pass an acceptable standard.

Result name	Mark range	Description
High distinction	85 - 100
Distinction	75 - 84
Credit	65 - 74
Pass	50 - 64
Fail	0 - 49	When you don’t meet the learning outcomes of the unit to a satisfactory standard.

For more information see guide to grades.

Late submission

In accordance with University policy, these penalties apply when written work is submitted after 11:59pm on the due date:

Deduction of 5% of the maximum mark for each calendar day after the due date.
After ten calendar days late, a mark of zero will be awarded.

Academic integrity

The Current Student website provides information on academic integrity and the resources available to all students. The University expects students and staff to act ethically and honestly and will treat all allegations of academic integrity breaches seriously.

We use similarity detection software to detect potential instances of plagiarism or other forms of academic integrity breach. If such matches indicate evidence of plagiarism or other forms of academic integrity breaches, your teacher is required to report your work for further investigation.

Use of generative artificial intelligence (AI) and automated writing tools

You may only use generative AI and automated writing tools in assessment tasks if you are permitted to by your unit coordinator. If you do use these tools, you must acknowledge this in your work, either in a footnote or an acknowledgement section. The assessment instructions or unit outline will give guidance of the types of tools that are permitted and how the tools should be used.

Your final submitted work must be your own, original work. You must acknowledge any use of generative AI tools that have been used in the assessment, and any material that forms part of your submission must be appropriately referenced. For guidance on how to acknowledge the use of AI, please refer to the AI in Education Canvas site.

The unapproved use of these tools or unacknowledged use will be considered a breach of the Academic Integrity Policy and penalties may apply.

Studiosity is permitted unless otherwise indicated by the unit coordinator. The use of this service must be acknowledged in your submission as detailed on the Learning Hub’s Canvas page.

Outside assessment tasks, generative AI tools may be used to support your learning. The AI in Education Canvas site contains a number of productive ways that students are using AI to improve their learning.

Learning support

Simple extensions

If you encounter a problem submitting your work on time, you may be able to apply for an extension of five calendar days through a simple extension.  The application process will be different depending on the type of assessment and extensions cannot be granted for some assessment types like exams.

Special consideration

If exceptional circumstances mean you can’t complete an assessment, you need consideration for a longer period of time, or if you have essential commitments which impact your performance in an assessment, you may be eligible for special consideration or special arrangements.

Special consideration applications will not be affected by a simple extension application.

Using AI responsibly

Co-created with students, AI in Education includes lots of helpful examples of how students use generative AI tools to support their learning. It explains how generative AI works, the different tools available and how to use them responsibly and productively.

Weekly schedule

WK	Topic	Learning activity
Week 01	1. Overview of course; 2. Introduction to Pervasive Computing and IoT	Lecture (2 hr)
Week 02	1. Vision of Pervasive Computing	Lecture (2 hr)
Week 02	1. Preparing for the unit (software and hardware). 2. Form groups	Tutorial (1 hr)
Week 03	1. Internet of Things; 2. Pervasive Computing Devices	Lecture (2 hr)
Week 03	Arduino Framework	Tutorial (1 hr)
Week 04	1. Network Architectures; 2. Connected Things	Lecture (2 hr)
Week 04	Arduino + Communication	Tutorial (1 hr)
Week 05	1. Network Architectures; 2. Connected Things (Part 2)	Lecture (2 hr)
Week 05	IoT Communication: Bluetooth, Wifi, Lora, etc.	Tutorial (1 hr)
Week 06	1. Pervasive Sensors and Actuators;	Lecture (2 hr)
Week 06	Pervasive sensors and actuator	Tutorial (1 hr)
Week 07	1. IoT Services;	Lecture (2 hr)
Week 07	Services: HTTP, NodeRED, etc.	Tutorial (1 hr)
Week 08	1. Cloud Techniques and Services	Lecture (2 hr)
Week 08	Cloud Services: Amazon AWS, Azure	Tutorial (1 hr)
Week 09	1. Rapid Prototyping: Laser cutting, 3D printing	Lecture (2 hr)
Week 09	Physical Prototyping	Tutorial (1 hr)
Week 10	1. Evaluation: Technical + Usability	Lecture (2 hr)
Week 10	Project work	Tutorial (1 hr)
Week 11	1. Pervasive Interfaces: Voice, sound, gesture	Lecture (2 hr)
Week 11	Cloud service 2	Tutorial (1 hr)
Week 12	1. Privacy and Security	Lecture (2 hr)
Week 12	Project Work	Tutorial (1 hr)
Week 13	1. Future Perspective and Review	Lecture (2 hr)
Week 13	Project Work	Tutorial (1 hr)

Attendance and class requirements

All the lectures/tutorials will be conducted in-person. Attendance is highly recommended. There will be in-class quizes. You can participate remotely in quizes if you coordinate your timing.

Study commitment

Typically, there is a minimum expectation of 1.5-2 hours of student effort per week per credit point for units of study offered over a full semester. For a 6 credit point unit, this equates to roughly 120-150 hours of student effort in total.

Required readings

All readings for this unit can be accessed through the Library’s reading list system Leganto, available on Canvas.

Learning outcomes

Learning outcomes are what students know, understand and are able to do on completion of a unit of study. They are aligned with the University's graduate qualities and are assessed as part of the curriculum.

Outcomes
Graduate qualities

At the completion of this unit, you should be able to:

LO1. analyse a context to define the overall goals of a new pervasive system
LO2. select suitable techniques for establishing a pervasive computing system and have the ability to make use of these techniques
LO3. apply standard design approaches to creating a pervasive interface, and to adapt these to the particular emerging demands of pervasive computing systems
LO4. evaluate pervasive interfaces, based upon both technical and user-based techniques, and be able to select the most appropriate technique for a particular situation and justify this selection
LO5. explain how technical and human factors influence aspects of design of interfaces
LO6. bring together design and evaluation techniques to create a prototype interface that is designed to address a given need
LO7. write a report on the prototype system created.

Graduate qualities

The graduate qualities are the qualities and skills that all University of Sydney graduates must demonstrate on successful completion of an award course. As a future Sydney graduate, the set of qualities have been designed to equip you for the contemporary world.

GQ1	Depth of disciplinary expertise Deep disciplinary expertise is the ability to integrate and rigorously apply knowledge, understanding and skills of a recognised discipline defined by scholarly activity, as well as familiarity with evolving practice of the discipline.
GQ2	Critical thinking and problem solving Critical thinking and problem solving are the questioning of ideas, evidence and assumptions in order to propose and evaluate hypotheses or alternative arguments before formulating a conclusion or a solution to an identified problem.
GQ3	Oral and written communication Effective communication, in both oral and written form, is the clear exchange of meaning in a manner that is appropriate to audience and context.
GQ4	Information and digital literacy Information and digital literacy is the ability to locate, interpret, evaluate, manage, adapt, integrate, create and convey information using appropriate resources, tools and strategies.
GQ5	Inventiveness Generating novel ideas and solutions.
GQ6	Cultural competence Cultural Competence is the ability to actively, ethically, respectfully, and successfully engage across and between cultures. In the Australian context, this includes and celebrates Aboriginal and Torres Strait Islander cultures, knowledge systems, and a mature understanding of contemporary issues.
GQ7	Interdisciplinary effectiveness Interdisciplinary effectiveness is the integration and synthesis of multiple viewpoints and practices, working effectively across disciplinary boundaries.
GQ8	Integrated professional, ethical, and personal identity An integrated professional, ethical and personal identity is understanding the interaction between one’s personal and professional selves in an ethical context.
GQ9	Influence Engaging others in a process, idea or vision.

Outcome map

Learning outcomes	Graduate qualities
Learning outcomes

Responding to student feedback

This section outlines changes made to this unit following staff and student reviews.

The contents has been updated since last run of the unit.

Disclaimer

The University reserves the right to amend units of study or no longer offer certain units, including where there are low enrolment numbers.

To help you understand common terms that we use at the University, we offer an online glossary.

Current students

Overview

Overview

Unit details and rules

Teaching staff

Assessment

Assessment

Assessment summary

Assessment criteria

Late submission

Academic integrity

Learning support

Learning support

Simple extensions

Special consideration

Using AI responsibly

Weekly schedule

Weekly schedule

Attendance and class requirements

Study commitment

Required readings

Learning outcomes

Learning outcomes

Graduate qualities

Outcome map

Responding to student feedback

Responding to student feedback

Disclaimer

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