Courses & Units

Internet of Things and Distributed Artificial Intelligence KIT317

Launceston, Hobart

Introduction

Internet of Things (IoT) is rising set of technologies that provides access to a large quantity of data through sensors. Such devices are ubiquitous today in industrial processes, vehicles, robots, environmental monitoring, farms, hospitals, and on our personal item such as phones. IoT enables users to visualize, monitor, analyse and predict aspects of their environments that would otherwise be impossible to do manually. The ability to connect devices to the internet allows humans to have access to data in real time. Large amount of data collected over time can lead to discovery of specific patterns using machine learning and artificial intelligence which could in turn lead to improvement of the system, the IoT is observing. Many standard technologies have been developed to empower IoT, such as low-cost micro-controllers and communication mechanisms such as LoRaWAN which impacts the development of distributed and intelligent IoT applications.

The aim of this unit is to explore modern technologies surrounding sensor networks with intelligent edge computing in context of IoT. This unit will refine critical thinking and skills when considering Internet of things applications. Also, based on practical field components such as micro-controllers, you will develop the skills to process the data generated in a distributed manner from IoT using Artificial Intelligence and Machine Learning methods

Summary

Unit name Internet of Things and Distributed Artificial Intelligence
Unit code KIT317
Credit points 12.5
College/School College of Sciences and Engineering
School of Information and Communication Technology
Discipline Information & Communication Technology
Coordinator Doctor Ananda Maiti
Available as student elective? Yes
Level Advanced

Availability

Location Study period Attendance options Available to
Launceston Semester 1 On-Campus International Domestic
Hobart Semester 1 On-Campus International Domestic

Key

On-campus
Off-Campus
International students
Domestic students
Note

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Key Dates

Study Period Start date Census date WW date End date
Semester 1 21/2/2022 22/3/2022 11/4/2022 29/5/2022

* The Final WW Date is the final date from which you can withdraw from the unit without academic penalty, however you will still incur a financial liability (see withdrawal dates explained for more information).

Unit census dates currently displaying for 2022 are indicative and subject to change. Finalised census dates for 2022 will be available from the 1st October 2021.

About Census Dates

Learning Outcomes

  • Build IoT using sensor networks and technology
  • Design, build and deploy efficient sensor networks fit for purpose.
  • Determining the correct technologies such as software architectures and data formats for IoT applications.
  • Analyse the data from sensor networks using artificial intelligence and machine learning methods.
Field of Education Commencing Student Contribution 1 Grandfathered Student Contribution 1 Approved Pathway Course Student Contribution 2 Domestic Full Fee
029999 $1,002.00 $1,002.00 not applicable

1 Please refer here more information on student contribution amounts.
2 Information on eligibility and Approved Pathway courses can be found here
If you have any questions in relation to the fees, please contact UConnect or more information is available on StudyAssist.

Please note: international students should refer to this page to get an indicative course cost.

Requisites

Prerequisites

12.5 credit points in KIT units at intermediate level AND 12.5 credit points in KIT, KGG or KMA units at intermediate level.

Teaching

Teaching Pattern

Lectures: 2 hrs/week (Weeks 1-13)
Tutorials: 2 hrs/week (Weeks 2-13)

AssessmentQuizzes (x2) (10%)|Examination (40%)|Assignment 1 (20%)|Assignment 2: Analysing and Reporting on Data (20%)|Workshop Exercises (10%)
TimetableView the lecture timetable | View the full unit timetable

Textbooks

Required

Required readings will be listed in the unit outline prior to the start of classes.

LinksBooktopia textbook finder

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