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Wireless Networking aka “Wireless for IoT Class” (OLD HOMEPAGE! It was taught in 2023!!)

Course code: CS4222/CS5422

Instructor: Professor Ambuj Varshney

Contact:, COM3: #02-25

Office Hours: Friday, 4 - 6 pm (By appointment, please send email)


This course offers a introduction to the field of wireless networking and the rapidly expanding world of the Internet of Things (IoT). It covers various layers of wireless networking, including physical, link, MAC, routing, and application, and discusses relevant wireless standards such as WiFi, Bluetooth, ZigBee, cellular networks for IoT (NB-IOT), and long-range standards such as LoRa. In addition, the course explores emerging technologies like LiFi (visible light communication) and backscatter. The course also focuses on the energy efficiency of IoT devices and the integration of sensing, communication, and processing. Throughout the course, students will have the opportunity to apply these concepts through hands-on projects that involve creating software prototypes on real IoT devices.



(CS2105 or EE3204/E or EE4204) and (EE2012/A or MA2216 or ST2131 or ST2334)

Preclusion CS5422 and EE5132


Lecture number Topic Slide ChatGPT Prompts Youtube ChatGPT Responses
1/ Week 1 Overview of internet of things, history and growth of wireless communication, electromagnetic signals and propagation, overview of various wireless technologies   Lecture 1 Prompts Part1, Part2, Part3 Responses
2/ Week 2 Signal propagation and mathematics, internet of things device basics, platforms, and contiki operating system   Lecture 2 Prompts Part1, Part2, Part3 Responses
3/ Week 3 Public/National Holiday (No lecture) - -    
4/ Week 4 Sensing, Actuation, and Battery Capacity   Lecture 3 Prompts Part 1, Part 2 Responses
5/ Week 5 Energy Efficiency, Power management, Backscatter communication   Lecture 4 Prompts Part 1, Part 2, Part 3  
6/ Week 6 Localization   Lecture 5 Prompts Part 1, Part 2, Part 3 Responses
7/ Week 7 No Lecture - -    
8/ Week 8 Communication Layers and Medium Access Control   Lecture 6 Prompts Part 1, Part 2a, Part 2b, Part 3 Not Planned
9/ Week 9 Medium Access Control for IoT   Lecture 7 Prompts Part 1 Not Planned
10/ Week 10 Discovery and Routing   Lecture 8 Prompts Part 1, Part 2 Responses
11/ Week 11 RPL, Case Study: Wide Area Networks   Lecture 9 Prompts Part 1, Part 2, Part 3 Not Planned
12/ Week 12 Case study: Bluetooth, Thread, Matter   Lecture 10 Prompts Part 1, Part 2, Part 3 Not Planned
13/ Week 13 Case study: ZigBee, WiFi, Cellular basics   Lecture 11 Prompts Part 1 , Part 2 Not Planned
14/ Week 14 Case study: Cellular, NB-IoT, End-of-course   Lecture 12 Prompts Part 1, Part Final Not Planned


The course has three mandatory assignment.

Week Number Assignment Deadline
Assignment 1 Assignment #1 Feb 10th 2023
Assignment 2 Assignment #2 March 10th 2023
Assignment 3 Assignment #3 April 2nd 2023


You can find your group number and project allotment in the following link.

Week Number Assignment Deadline
Project Project April 21st 2023

NOTE: You can submit project after deadline with a penality of 10% of allocated marks for the project. However, we will not accept project after 28th of April 2023


Week Number Link Solution
Week 3 Tutorial #1  
Week 4 Tutorial #2  
Week 5 Tutorial #3  
Week 6 Tutorial #4  
Week 7 No Tutorial No Tutorial
Week 8 No Tutorial No Tutorial
Week 9 Tutorial #5  
Week 10 Tutorial #6  
Week 11 Tutorial #7  
Week 12 Tutorial #8  
Week 13 Tutorial #9 (Practice Exam Questions) (Optional, NO attendance)  
Week 14 (Tutorial #10) Open Session/Practice Exam Questions (Optional, NO attendance) (Updated V2)

You can also find archive of tutorial presentations used by teaching assistants here: Malaika, Ayanga, Kanav


We will use the following platform for conducting projects in the course:

We also have limited quantities of following platform for ambitious group of students:


Here are important information regarding the final assesment:



Wireless signal strength calculations

Interviews and Keynotes

The following link contains relevant videos for the course: Video list


The course has its own dedicated Slack page, which serves as a platform for communication among the instructor, teaching assistants, and students. Additionally, students are encouraged to share news and articles pertaining to the topics of IoT and wireless within the Slack workspace.


The assessment of this course will be conducted as follows:

The detailed breakdown of various components is as follows:

Component Weightage (in percentage)
Assignment 1 3%
Assignment 2 12%
Assignment 3 13%
Tutorials 5%
Project 32%
Bonus 5%
Final assesment 30%

NOTE: We had to deviate from original allocation of marks, as we had cancelled Assignment 4

Policy regarding bonus mark allotment: There are multiple ways for you to get bonus marks:


The course does not have a textbook. Nonetheless, we recommend the following books for learning about wireless communication and networking and Internet of Things (IoT).


We are experiencing a major shift in the computing landscape with the emergence of advanced tools such as Language Language Models (LLMs) such as OpenAI ChatGPT. These tools are poised to have a significant impact on the field, and we are embracing this change by incorporating them into our course curriculum. The CS4222/5222 course will be one of the first (if not the first) course in the world at a major univeristy to actively use OpenAI’s ChatGPT in teaching wireless technology. Students will be provided with “prompts” to work with the model and we may also propose projects for more ambitious students to utilize ChatGPT. However, we will not be using ChatGPT for tutorials in this iteration of the course. It is important for students who utilize ChatGPT to clearly indicate that they have employed the tool.

IMPORTANT: It is important to note that Large Language Models (LLMs) are still in the early stages of development and may not always produce accurate results. Therefore, these tools should only be used as a supplement, not as the primary source of information, and one should use them with caution.


Parts of the course were adapted from courses previously offered by: Prof. Pat Pannuto (University of California, San Diego), Branden Ghena (Northwestern University), Prof. Prabal Dutta (University of California, Berkeley) and Prof. Mun Choon Chan (National University of Singapore). Some of the images are generated using Open AI Dall-e.