Courses & Units
Internet of Things and Distributed Artificial Intelligence KIT317
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 an elective? | Yes |
| Delivered By | University of Tasmania |
| Level | Advanced |
Availability
| Location | Study period | Attendance options | Available to | ||
|---|---|---|---|---|---|
| Hobart | Semester 1 | On-Campus | International | Domestic | |
| Launceston | 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 | 20/2/2023 | 21/3/2023 | 10/4/2023 | 28/5/2023 |
* 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 (refer to How do I withdraw from a unit? for more information).
Unit census dates currently displaying for 2023 are indicative and subject to change. Finalised census dates for 2023 will be available from the 1st October 2022. Note census date cutoff is 11.59pm AEST (AEDT during October to March).
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 1,3 | Grandfathered Student Contribution 1,3 | Approved Pathway Course Student Contribution 2,3 | Domestic Full Fee 4 |
|---|---|---|---|---|
| 029999 | $1,037.00 | $1,037.00 | not applicable | $2,522.00 |
1 Please refer to more information on student contribution amounts.
2 Please refer to more information on eligibility and Approved Pathway courses.
3 Please refer to more information on eligibility for HECS-HELP.
4 Please refer to more information on eligibility for FEE-HELP.
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 What is an indicative Fee? to get an indicative course cost.
Requisites
Prerequisites
KIT205 OR KIT206 OR KIT202Teaching
| Teaching Pattern | Lectures: 2 hrs/week (Weeks 1-13) |
|---|---|
| Assessment | Quizzes (x2) (10%)|Examination (40%)|Assignment 1 (20%)|Assignment 2: Analysing and Reporting on Data (20%)|Workshop Exercises (10%) |
| Timetable | View 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. |
Links | Booktopia textbook finder |
|---|
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