Courses & Units

Internet of Things and Distributed Artificial Intelligence KIT317


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 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


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


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


International students
Domestic students

Please check that your computer meets the minimum System Requirements if you are attending via Distance/Off-Campus.

Units are offered in attending mode unless otherwise indicated (that is attendance is required at the campus identified). A unit identified as offered by distance, that is there is no requirement for attendance, is identified with a nominal enrolment campus. A unit offered to both attending students and by distance from the same campus is identified as having both modes of study.

Key Dates

Study Period Start date Census date WW date End date
Semester 1 26/2/2024 22/3/2024 15/4/2024 2/6/2024

* 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 2024 are indicative and subject to change. Finalised census dates for 2024 will be available from the 1st October 2023. Note census date cutoff is 11.59pm AEST (AEDT during October to March).

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.

Fee Information

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 $1,118.00 $1,118.00 not applicable $2,648.00

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.



KIT205 OR KIT206 OR KIT202


Teaching Pattern

On-Campus enrolments in Hobart and Launceston: 

  • Lectures: 2 hours weekly lecture
  • Tutorials: 2 hrs/wk - For this unit, students are expected to attend on-campus tutorials;
AssessmentWorkshop Exercises (10%)|Assignment 1: Basic Sensor Programming with Web Authentication (20%)|Quizzes (x2) (20%)|Assignment 2: Faulty sensor detection (25%)|Assignment 3: Intelligent IoT system (25%)
TimetableView the lecture timetable | View the full unit timetable



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

LinksBooktopia textbook finder

The University reserves the right to amend or remove courses and unit availabilities, as appropriate.