SEE711 - IoT Systems Engineering

Unit details

Year:

2024 unit information

Enrolment modes:Trimester 2: Waurn Ponds (Geelong), Online
Credit point(s):1
EFTSL value:0.125
Unit Chair:Trimester 2: Bipasha Kashyap
Prerequisite:

For students enrolled in S460, S461, S462, S463, S465, S466, S467: SEE312 and completion of 18 credits points or Unit Chair approval.

For students enrolled in S550, S751, S752, S756, S757, S758, S759: Nil.

For students enrolled in courses outside of the School of Engineering: Unit Chair approval.

Corequisite:Nil
Incompatible with: Nil
Typical study commitment:

Students will on average spend 150 hours over the teaching period undertaking the teaching, learning and assessment activities for this unit.

Educator-facilitated (scheduled) learning activities - on-campus unit enrolment:

1 x 1 hour seminar and 1 x 1 hour online practical experience (studio) per week.

Educator-facilitated (scheduled) learning activities - online unit enrolment:

1 x 1 hour online seminar and 1 x 1 hour online practical experience (studio) per week (online).

Content

This unit explores the recent advances in the area of networking for sensory devices. There have been developments in both sensing and networking at a remarkable rate in the past few years. Modern day requirements have fuelled the amalgamation of these traditionally separate technologies and resulted widespread commercial and research interest in the subsequent, rapidly emerging area of sensor networks. This unit will particularly focus on Internet of Things (IoT) based Wireless Sensor Networks (WSN) and look at the underlying issues related to implementations in the combined area such as sensor data analytics and network design. Some of the commercially available systems will be introduced and the overall unit will be presented in a cohesive and application-oriented manner.

ULO These are the Learning Outcomes (ULO) for this unit. At the completion of this unit, successful students can: Deakin Graduate Learning Outcomes
ULO1

Configure the system platform for a specific Internet of Things (IoT) based wireless sensor network (WSN) application.

GLO1: Discipline-specific knowledge and capabilities
GLO4: Critical thinking

ULO2

Apply data acquisition and data-preprocessing techniques for enhanced system performance.

GLO1: Discipline-specific knowledge and capabilities
GLO4: Critical thinking
GLO5: Problem solving

ULO3

Analyse the data collected by an application and capture the application specific characteristics of interest.

GLO1: Discipline-specific knowledge and capabilities
GLO4: Critical thinking

ULO4

Design and implement the IoT based WSN project with innovative solutions to problems or analytical tools to improve the usefulness of the data collected.

GLO1: Discipline-specific knowledge and capabilities
GLO4: Critical thinking
GLO5: Problem solving

ULO5

 

Demonstrate an understanding of the IoT based WSN system and its development methods, with regard to a particular engineering project, via effective communication skills.

GLO1: Discipline-specific knowledge and capabilities
GLO2: Communication
GLO4: Critical thinking

Assessment

Assessment Description Student output Grading and weighting
(% total mark for unit)
Indicative due week
Assessment 1
Video presentation (Project A)
5-minute video 30% Week 6
Assessment 2
Project report (Project A)
2000 word report 30%  Week 7
Assessment 3
Video presentation (Project B)
5-minute video 20% Week 10
Assessment 4
Project report (Project B)
2000 word report 20% Week 11

The assessment due weeks provided may change. The Unit Chair will clarify the exact assessment requirements, including the due date, at the start of the teaching period.

Learning Resource

There is no prescribed text. Unit materials are provided via the unit site. This includes unit topic readings and references to further information.

Unit Fee Information

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