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DEGREE REGULATIONS & PROGRAMMES OF STUDY 2020/2021

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DRPS : Course Catalogue : School of Informatics : Informatics

Undergraduate Course: Principles and Design of IoT Systems (INFR11150)

Course Outline
SchoolSchool of Informatics CollegeCollege of Science and Engineering
Credit level (Normal year taken)SCQF Level 11 (Year 4 Undergraduate) AvailabilityNot available to visiting students
SCQF Credits20 ECTS Credits10
SummaryThe course - 'Principles and Design of the Internet of Things' (PDIoT) is concerned with the emerging discipline of digitising the physical world with networks of wireless sensors, analysing the sensor data using machine learning techniques to extract actionable information, and influencing the physical world via actuators, with an optional human in the loop. The course imparts foundational concepts in IoT through personal research distilled in the form of two survey papers on foundational topics in IoT, and students working in pairs gain hands-on experience by realising a healthcare application idea as a demonstratable IoT system using wearable sensors by the send of the semester.
Course description The course aims to deliver a sound understanding of the design and analysis of Internet of Things systems through personal research and practice. The research in a choice of selected foundational topics in IoT provides the foundational knowledge distilled in the form of two 3000-word survey papers.

The students conduct a major piece of coursework working in pairs to develop an IoT application using wearable sensors. Students will experience all the stages in the design and implementation of a complex system, from its specification to the demonstration of a working prototype. They will be exposed to aspects of embedded systems programming, sensor data analytics using machine learning methods, user interface design, system integration and testing. Each pair will demonstrate a working prototype at the end of Semester 1 and deliver a written report at the start of Semester 2.

Each student pair is given a set of Mbed development board (NRF52-DK), Inertial Measurement Unit (MPU-9250) with 3-axis accelerometer and gyroscope sensors, and an on-line software development environment the ARM Mbed compiler. The task will be to design, implement and demonstrate a Step Tracker for walking on level ground, running and climbing stairs using the wearable sensor which interfaces to an Android App.

The final report will be due at the end of Week 1 in the second semester.
Entry Requirements (not applicable to Visiting Students)
Pre-requisites Co-requisites Students MUST also take: Introductory Applied Machine Learning (INFR10069) OR Introductory Applied Machine Learning (INFR11182)
Prohibited Combinations Other requirements Students should be proficient in Java/Python programming.
Course Delivery Information
Academic year 2020/21, Not available to visiting students (SS1) Quota:  None
Course Start Semester 1
Timetable Timetable
Learning and Teaching activities (Further Info) Total Hours: 200 ( Lecture Hours 10, Supervised Practical/Workshop/Studio Hours 10, Feedback/Feedforward Hours 0.5, Programme Level Learning and Teaching Hours 4, Directed Learning and Independent Learning Hours 175 )
Assessment (Further Info) Written Exam 0 %, Coursework 100 %, Practical Exam 0 %
Additional Information (Assessment) The course is assessed on the basis of practical coursework (70% of the overall marks) and two survey papers (30%) on foundational topics (max 5000 words) in the area of Internet of Things.
Feedback There will be a course feedback opportunity for the students mid-way and at the end of the course. There will be a formative feedback on the coursework provided to the students in Week 6 and Week 10.
No Exam Information
Learning Outcomes
On completion of this course, the student will be able to:
  1. Experience the end-to-end design, implementation and demonstration of a typical Internet of Things system, and gaining skills in embedded programming for the collection and processing of sensor data, processing and analysis using machine learning methods, and, displaying the results in an Android mobile application.
  2. Gain knowledge in a selection of methods for pre-processing, feature extraction and classification of time-series sensor data, and their efficacy when applied to noisy sensor data.
  3. Experience using tools such as compilers for IoT development board using inertial sensors, system-level simulators, and Android mobile applications development.
  4. Learn the process of gathering information from primary sources such as research papers and reports forcomparative study in selected foundational topics in IoT which are distilled in two survey papers.
  5. Experience working with another team member with complimentary skill sets; develop skills in project management, requirements capture, negotiations, and oral and written presentations.
Reading List
None
Additional Information
Graduate Attributes and Skills Develop communication skills (oral/written) for capturing the requirements and specification of complex systems.
Develop inter-personal skills when working with another team member in dividing the work up and dealing with delays, setbacks and other issues.
Develop skills in project management, requirements capture and negotiations.
KeywordsPDIoT,Internet of Things
Contacts
Course organiserProf D K Arvind
Tel: (0131 6)50 5176
Email: d.k.arvind@ed.ac.uk
Course secretaryMiss Clara Fraser
Tel: (0131 6)51 4164
Email: clara.fraser@ed.ac.uk
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