Principles and Design of IoT Systems

PDIOT: Course Contacts

flittlet — Thu, 03 Aug 2023 15:22:10 +0000

PDIOT: Course Contacts flittlet Thu, 03/08/2023 - 16:22

Course Organiser

D. K. Arvind

d.arvind@ed.ac.uk

Room 2.18

Informatics Forum

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PDIOT: Assessment

flittlet — Thu, 03 Aug 2023 15:22:07 +0000

PDIOT: Assessment flittlet Thu, 03/08/2023 - 16:22

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PDIOT: Coursework

flittlet — Thu, 03 Aug 2023 15:22:02 +0000

PDIOT: Coursework flittlet Thu, 03/08/2023 - 16:22

PDIoT has 3 coursework components: data collection, research essay and the activity classification system, which each group will develop. Instructions for parts 1 and 3 can be found at the links below.

Coursework 1

https://twisty-mayflower-47b.notion.site/PDIoT-Coursework-1-15-e323ee084f684d169d923c59324cf5ed?pvs=4

You can find the code for the PDIoT Android app here:

https://github.com/specknet/pdiotapp

You will also need to fork the shared data collection respository.

https://github.com/specknet/pdiot-data

Coursework 3

https://twisty-mayflower-47b.notion.site/PDIoT-Coursework-3-70-a1c8f2c1701344f7bc50d74496641f43?pvs=4

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PDIOT: Lab Sessions

flittlet — Thu, 03 Aug 2023 15:22:01 +0000

PDIOT: Lab Sessions flittlet Thu, 03/08/2023 - 16:22

The following documents contain instructions for the weekly lab sessions.

Jupyter notebooks:

https://github.com/specknet/pdiot-practical/blob/master/Labs/PDIoT_Lab2_data_cleaning_trimming.ipynb

https://github.com/specknet/pdiot-practical/blob/master/Labs/PDIoT_Lab2_data_loading_visualization.ipynb

Lab 3

https://github.com/specknet/pdiot-practical/blob/master/Labs/Week%203%20Lab.md

Lab 4

https://github.com/specknet/pdiot-practical/blob/master/Labs/Week%204%20Lab.md

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PDIOT: Principles and Design of IoT Systems

flittlet — Thu, 03 Aug 2023 15:21:59 +0000

PDIOT: Principles and Design of IoT Systems flittlet Thu, 03/08/2023 - 16:21

Welcome to Principles and Design of IoT Systems

For the inaugural meeting of PDIoT course, all students should come to Appleton Tower room 3.09 on Wednesday, 20 September at 10:00. There will be an introduction to the course, followed by formation of groups and collection of hardware. In subsequent weeks each group can choose between 10:00, 11:00 and 12:00 on Wednesdays for the compulsory lab session.

The 2023-24 course document is now available here:

https://github.com/specknet/pdiot-course-document/blob/main/PDIOTCoursework2023-24.pdf

Learning Outcomes

On successful completion of this course, you should 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.

Course Outline

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 group will demonstrate a working prototype at the end of Semester 1 and deliver a written report at the start of Semester 2.

This year the task will be to design, implement and demonstrate a real-time Human Activity Classification system using Inertial Motion Unit (IMU) sensors which stream data to an Android app using Bluetooth LE.

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Principles and Design of IoT Systems

PDIOT: Course Contacts

Course Organiser

PDIOT: Assessment

PDIOT: Coursework

Coursework 1

Coursework 3

PDIOT: Lab Sessions

Lab 1.1

Lab 1.2

Lab 2

Lab 3

Lab 4

PDIOT: Principles and Design of IoT Systems

Welcome to Principles and Design of IoT Systems

Learning Outcomes

Course Outline