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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) AvailabilityAvailable to all students
SCQF Credits20 ECTS Credits10
SummaryThe course 'Principles and Design of the Internet of Things Systems' (PDIoT) is concerned with the emerging discipline of digitising the physical world with wireless sensors, analysing the sensor data to provide actionable information, and influencing the physical world via actuators, with an optional human in the loop. The course imparts foundational concepts in IoT in a series of 10 lectures and students gain hands-on experience by realising their application idea as a demonstratable IoT system prototype. The lectures will be illustrated with a number of IoT case studies undertaken by the lecturer over the past fifteen years.
Course description The course aims to deliver a sound understanding of the design and analysis of Internet of Things through lectures and practice. The lectures provide the foundational knowledge in sensors and actuators, fusion of data from multiple sensors, sensor data calibration and topics in sensor data analytics: pre-processing and extraction of features in time-series sensor data, and classification methods using a selection of machine learning techniques. The students conduct a major piece of coursework working in pairs to develop an application using an IoT platform together with a mobile application. 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, data collection, algorithm development, user interface design, mobile application design, system integration and testing. The goal is to develop a step counter which should detect walking on level ground and climbing stairs. Each student pair will be given an NRF52DK dev board with Bluetooth wireless connection, and triaxial accelerometer and gyroscope sensors. On-device firmware will be developed using the mbed platform and Android will be used for an accompanying mobile application. Each pair will demonstrate a working prototype at the end of 10 weeks and deliver a written report by 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)
Prohibited Combinations Other requirements Students should be proficient in Java/Python programming.
Information for Visiting Students
Pre-requisitesMust satisfy the entry requirements.
High Demand Course? Yes
Course Delivery Information
Academic year 2018/19, 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, Summative Assessment Hours 0.5, Programme Level Learning and Teaching Hours 4, Directed Learning and Independent Learning Hours 175 )
Assessment (Further Info) Written Exam 30 %, Coursework 70 %, Practical Exam 0 %
Additional Information (Assessment) Written Examination: 30%
Practical Examination: 0%
Coursework: 70%

[Technical evaluation 60%]: Completion of the project; degree of difficulty; quality and amount of work; justification of design decisions; design for reusability.
[Presentation 20%]: Quality of the oral presentation, website and report, and references to other sources.
[Analysis 20%]: Critical analysis using both quantitative methods and reflection on design decisions.

Semester 1; Demonstration of working prototype on the Wednesday in Week 10; Report due at 4pm on the Friday in Week 1 in Semester 2; written examination in April.
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.
Exam Information
Exam Diet Paper Name Hours & Minutes
Main Exam Diet S2 (April/May)2:00
Learning Outcomes
On completion of this course, the student will be able to:
  1. An understanding of the constituent parts of a typical IoT system, a selection of sensors and actuators, and an appreciation of methods employed to address the security and privacy issues in IoT. Case studies will illustrate the application of IoT in healthcare, digital media and environmental monitoring.
  2. Knowledge of a selection of sensor fusion algorithms, and data analytic methods for the pre-processing of time-series sensor data, feature extraction and their classification; the application of these methods in practice will be illustrated with case studies.
  3. Experience of the practical issues involved in the specification, design and implementation of an IoT system based on his/her application idea.
  4. Experience working with another team member with complimentary skill sets, and develop skills in project management, requirements capture and negotiations.
  5. Experience using tools such as compilers for IoT development board using inertial sensors, system-level simulators and web-authoring tools for the final report.
Reading List
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
Course organiserProf D K Arvind
Tel: (0131 6)50 5176
Course secretaryMr Gregor Hall
Tel: (0131 6)50 5194
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