BDA324 - Intelligent Sensor Networks

Credit Points: 15 credit points

Workload: 48 hours

Prerequisite: BDA214 Machine learning

Co-requisite: N/A

Aims & Objectives

This is an elective unit out of a total of 24 units in the Bachelor of Data Analytics (BDA). This unit addresses the BDA course learning outcomes and complements other courses in related fields by developing students’ specialised knowledge of advanced intelligent sensor network applications to address challenging problems in data analytics and other similar disciplines. For further course information refer to: http://www.mit.edu.au/study-with-us/programs/Bachelor-DA. This unit is part of the AQF level 7 (BDA) course.

Students will learn to plan, design, implement and evaluate advanced intelligent sensor network applications in a complex and challenging business environment. They will gain knowledge of design strategies and deployment methodologies in sensor networks in data analytic applications. Students will gain hands on practical experience in deploying and testing sensor network solutions to industrial applications that require critical analysis and technical competencies.

This unit will cover the following topics:

  • Distributed computing and sensor communications
  • Big data analytics and sensor networks
  • Cloud and fog computing infrastructure for large sensor networks
  • Dynamic and approximate learning of sensory data
  • Sensor applications in cellular communication systems and smart cities
  • Future human-centric intelligent systems

Learning Outcomes

4.1 Course Learning Outcomes
The Course learning outcomes applicable to this unit are listed on the Melbourne Institute of Technology’s website: www.mit.edu.au 

4.2 Unit Learning Outcomes
At the completion of this unit students should be able to:
a. Comprehend a range of intelligent sensor network tools and applications in industry applications.;
b. Select and apply appropriate intelligent sensor devices and communication networks to meet business requirements;
c. Deploy and monitor sensory network applications in industry applications with a good understanding of industry era 4.0 standards;
d. Evaluate intelligent sensor solution in consideration of business practices and technological landscapes;
e. Recommend appropriate sensor network solutions to a complex industry application. 

Weekly Topics

This unit will cover the following content:

Week Topics
1 Distributed computing and sensor communications
2 Big data analytics and sensor networks
3 IoT strategy and implementation
4 IoT system quality assurance and business continuity
5 Cloud computing infrastructure for large sensor networks
6 Fog computing infrastructure for large sensor networks
7 Distributed and federated learning in large sensor networks
8 Extreme learning machine in large sensor networks
9 Distributed and approximate learning of sensory data
10 Dynamic learning with heterogeneous sensory data
11 Spatial big data analytics for cellular communication systems
12 Future human-centric intelligent systems

Assessment

Assessment Task Due Date A B Learning Outcomes Assessed
Formative Assignment 1 Part A
Assignment 1 Part B		 Week 3
Week 8		 5%
10%		   a-c
Assignment 2 Week 11 25%   d-e
Laboratory participation & submission Week 2 - 11 10%   a-e
Final Examination (2 hours) End of trimester   50% a-e
TOTALS   50% 50%  

Task Type: Type A: unsupervised, Type B: supervised.

Class Participation and Contribution
This unit has class participation and student contribution as an assessment. The assessment task and marking rubric will follow the Guidelines on Assessing Class Participation (https://www.mit.edu.au/about-us/governance/institute-rules-policies-and-plans/policies-procedures-and-guidelines/Guidelines_on_Assessing_Class_Participation). Further details will be provided in the assessment specification on the type of assessment tasks and the marking rubrics.

Presentations (if applicable)
For presentations conducted in class, students are required to wear business attire.

Textbook and Reference Materials

Textbook:
References:

  • Vlasios Tsiatsis, Stamatis Karnouskos, Jan Holler, David Boyle, Catherine Mulligan (Author), Internet of Things: Technologies and Applications for a New Age of Intelligence, Academic Press, 2018 
  • Zhongzhi Shi, Advanced Artificial Intelligence (Second Edition), World Scientific Publishing Company, August 2019 

Adopted Reference Style: IEEE
 

Graduate Attributes

MIT is committed to ensure the course is current, practical and relevant so that graduates are “work ready” and equipped for life-long learning. In order to accomplish this, the MIT Graduate Attributes identify the required knowledge, skills and attributes that prepare students for the industry.
The level to which Graduate Attributes covered in this unit are as follows:

Ability to communicate Independent and Lifelong Learning Ethics Analytical and Problem Solving Cultural and Global Awareness Team work Specialist knowledge of a field of study

Legend

Levels of attainment Extent covered
The attribute is covered by theory and practice, and addressed by assessed activities in which the students always play an active role, e.g. workshops, lab submissions, assignments, demonstrations, tests, examinations.
The attribute is covered by theory or practice, and addressed by assessed activities in which the students mostly play an active role, e.g. discussions, reading, intepreting documents, tests, examinations.
The attribute is discussed in theory or practice; it is addressed by assessed activities in which the students may play an active role, e.g. lectures and discussions, reading, interpretation, workshops, presentations.
The attribute is presented as a side issue in theory or practice; it is not specifically assessed, but it is addressed by activities such as lectures or tutorials.
The attribute is not considered, there is no theory or practice or activities associated with this attribute.