ELEC 3520 – IoT and Cyber-Physical Systems

(University of Colorado Denver - USA)

Instructor Contact Information

Instructor: Thuan Dinh Do

Email: thuandinh.do@ucdenver.edu

Office: NC2420

Office Hours: TH, F 10-12 (MT)

TA:

Mr. Ryan Eskola

ryan.eskola@ucdenver.edu

The office hours schedule is as follows:

3:30-5pm Mondays and Wednesdays, NC 2609 (need appointment)

This course presents intelligent systems that go along with applications to allow Internet-of- Things (IoT) devices and cyber-physical entities to be connected with existing systems. An intelligent system consists of a collection of computing devices communicating with one another and capable of interacting with the physical world. The IoT systems are designed from simple case to complex systems (so-called IoT levels). This course introduces domain specific Internet of Things and their real-world applications. The course explores the system design and software development process through generic design methodology for IoT to implement real-time IoT applications in real-world scenarios. Topics include multiple hardware and software systems necessary to perform sensing, actuation, processing, and communication with connected computing devices.

This course meets in person for Fall 2022, MW12:30pm- 1:45pm.

After the start of the semester, all students must be prepared for on campus learning, no requested exceptions.

Learning Outcomes

1. Design, test, and debug embedded IoT software and devices

2. Analyze and assess IoT and cyber-physical trade-offs and systems

3. Construct an embedded system with sensor peripherals to collect data from and control physical world environments

4. Utilize data communication protocols (node-to-node, node-to-cloud) within an IoT & cyber-physical system

5. Specify, implement, and deploy a scalable IoT infrastructure to supports an edge-to-cloud engineering solution

6. Examine the operation of IoT/sensor devices and the collection of data with respect to privacy/regulations of various communities and stakeholders

1. Course Description

This course presents intelligent systems that go along with applications to allow Internet-of- Things (IoT) devices and cyber-physical entities to be connected with existing systems. An intelligent system consists of a collection of computing devices communicating with one another and capable of interacting with the physical world. The IoT systems are designed from simple case to complex systems (so-called IoT levels). This course introduces domain specific Internet of Things and their real-world applications. The course explores the system design and software development process through generic design methodology for IoT to implement real-time IoT applications in real-world scenarios. Topics include multiple hardware and software systems necessary to perform sensing, actuation, processing, and communication with connected computing devices.

2.Course Overview

The course focuses on building IoT systems that connect sensors and controls in the physical world with applied computing systems. The goal is to construct systems that convert information to data and have that data analyzed in various domains of use. Examples of IoT and cyber-physical systems include industrial, energy, healthcare, smart-city applications. Case studies are discussed to deal with useful applications in fields of smart cities, logistics, home automation, smart environment, retail, smart energy, smart agriculture, industrial control and smart. The students are encouraged to study advanced topics on IoT including IoT data analytics and machine learning.

3.Learning Outcomes

Assessment of student performance:

1. Design, test, and debug embedded IoT software and devices

2. Analyze and assess IoT and cyber-physical trade-offs and systems

3. Construct an embedded system with sensor peripherals to collect data from and control physical world environments

4. Utilize data communication protocols (node-to-node, node-to-cloud) within an IoT & cyber-physical system

5. Specify, implement, and deploy a scalable IoT infrastructure to supports an edge-to-cloud engineering solution

6. Examine the operation of IoT/sensor devices and the collection of data with respect to privacy/regulations of various communities and stakeholders

4.Course Prerequisites

No prerequisite.

5.Course Credits

Semester Hours: 3

6.Required Text and Material Access

Internet of Things: A Hands-on Approach, Arshdeep Bahga and Vijay Madisetti, Hands on Books Series, August 2014. http://www.hands-on-books-series.com/iot.html;

online resources: http://www.hands-on-books-series.com/students.html

Canvas - the university's Learning Management System (https://ucdenver.instructure.com/

7.Tentative Course Schedule:

See course map provided on first day of class for overview.

Week

1 Concept

2 Introduction to Internet of Things

3 Domain Specific IoTs

4 IoT and M2M

5 IoT System Management

6 IoT Platforms - Design Methodology

7 IoT Systems - Logical Design (i.e. using Python)

8 IoT Physical Devices and Endpoints

9 IoT Physical Servers and Cloud

9 Exam

10 Case Studies Illustrating IoT Design

11 Case Studies Illustrating IoT Design

12 Data Analytics, Machine Learning for IoT

13 Tools for IoT

14 Engineering project

15 Engineering project

16 Final Exam

8. Assignments

Homework: Assigned each week via the Canvas course website. Homework is due precisely at stated deadline and will never be emailed to instructor

Quizzes: Potential for quiz assessments at any time during lecture or online.

Project: A semester-end project involving process design. Before students begin working in developing a system, they need to submit 1-page project proposal containing topic, objective, and specifications for projects. Also, students are required to upload demo video and report for projects.

Basis for Final Grade: Student grades by the percentage given in the following tables.

9. Grade Dissemination

Graded assignments are returned in a timely manner to students with grades. The accumulation of grades will be available through the course’s Canvass course system.

10. Course Policies

Attendance: Students are expected to attend/view all lectures, as participation in lecture is important to the student’s success in the course. Students are also expected to arrive to class on time. If a student is to miss lectures, they should inform the instructor ahead of time with an excuse. If this is not possible due to illness, the student should inform the instructor when possible.

Late Work Policy: Homework assignments are submitted on-time, else it will be considered late. A student’s score will be reduced by a late penalty for submitting work.

Exam Policy: Exam guidelines will be provided for each exam. In general, no electronic devices (phones, computers, watches) are allowed. If any student uses an un-authorized device during the exam, their score is recorded as 0. If a student has a known conflict with an in-class exam, the student must inform the instructor in writing to request approval prior to the exam. If a student misses an exam due to illness, the student should contact the instructor before the exam, and provide a note from a medical doctor. Make up exams for in-class exams will only be given to students with pre-approved excuses, documented medical excuses, or guidelines of University Policy (e.g., three finals scheduled in one day.)

Student Honor Code: Students should be familiar with the College of Engineering and Applied Sciences student honor code. All honor code rules will be adhered to in this class. Assignments handed in by students must be his/her own work. While learning with fellow students is encouraged, copying of any portion of another student or person (tutor, Chegg) is a violation of the CEAS Student Honor Code.

Cheating: Students that turn in assignments that look near-identical to other students, will receive 0 credit. It is highly recommended to seek help, but copying identical work

or making variable changes in attempts to disguise solution will not be tolerated.

Disability and Access: The University of Colorado Denver is committed to ensuring the full participation of all students in its programs, including students with disabilities. Please contact Disability Resources and Services (DRS), Academic Building 1, Suite 2116, and at disabilityresources@ucdenver.edu and meet with the instructor to coordinate the necessary learning requirements for this course.

Mental Health Resources:

CU Denver faculty and staff understand the stress and pressure of college life. Students experiencing symptoms of anxiety, depression, substance use, loneliness or other issues affecting their mental well-being, have access to campus support services such as the Student and Community Counseling Center, the Wellness Center and the Office of Case Management. Students also have access to the You@CUDenver on-line well-being platform available 24/7. More information about mental health education and resources can be found at Lynx Central and CU Denver’s Health & Wellness page. Students in imminent crisis can contact Colorado Crisis Services for immediate assistance 24/7 or walk-in to the counseling center during regular business hours.

• Homework:

• Projects

- Time: 15 weeks

- 1-2 students/ 1 project

- Final report in IEEE paper format, videos, demo,…

- Project presentations : Time TBD

- Topics: Applications of machine learning in emerging IoT systems.

Ø Sign up for Project in class day/email.

Ø 10-15 minute presentation (including all project members)

Ø 3-4 minutes for questions

Ø You will be expected to attend at least 5 other presentations and actively participate in discussion