CS 537 Spring 2026 - Advanced Topics in IoT (Internet of Things) - Multimedia Systems

Class Location: 

Wednesdays: Classroom B, 4th Floor, Discovery Partners Institute (DPI), Chicago (200 S. Wacker Drive) - for Urbana students see zoom 

Fridays: 0220 Siebel Center (Basement),  Siebel School of Computing and Data Science, Urbana - for Chicago students see zoom

zoom links will be posted on the campuswire discussion platform.

Class Time: 9:30am to 10:45am 

Office Hours (Instructor): 

Wednesdays: 10:45am to noon, office 2008 DPI (20th Floor), Chicago in person or via zoom (Urbana only via zoom)

Fridays: 10:45am to noon, office 3104 Siebel Center, Urbana in person or via zoom (Chicago only via zoom

Instructor: Prof. Klara Nahrstedt (klara@illinois.edu)

Teaching Assistant: No TA - for appointments with instructor outside of class and office hours, please, contact Kristin Irle (kirle@illinois.edu)

Reading List: will be posted on February 20, 2026

Course Overview:

This course will focus on multimedia systems which are one important category of IoT systems. 

Goal: The course on advanced topics in Internet of Things (IoT), Multimedia Systems,  will discuss algorithms, protocols, architectures, systems, and infrastructures, selected from areas of current research such as: IoT sensors representations and their compression techniques,  IoT analytics with diverse machine learning algorithms, IoT Systems with their  IoT-edge-cloud computing infrastructures, IoT networks, IoT sensing, IoT system robustness, reliability, and security techniques, and IoT systems supporting XR data. Students will read and discuss recent research papers and conduct a semester-long research project. 

Course Elements:  We will take the end-to-end approach and explore an integrated view of multimedia IoT sensing, networking, system architectures, and evaluations, focused on time-series IoT data streams such as video, audio, IMU sensors, and other time-sensitive IoT streams. The topics will include (1)  IoT data representation for IOT devices including 2D and  video cameras, 1D audio, IMU and other 1D IoT devices (e.g., temperature, humidity),  (2) advanced compression techniques for multimedia IoT streams, including basic coding techniques and advanced techniques such as H.264/H.265, MPEG4/HVEC, MP3, (3) Machine Learning Techniques for IoT Data Analytics, (4) IoT network for video and other IoT data streaming, (5) IoT network transport protocols such as DASH and QUIC, (6) edge-cloud computing systems for IoT Data Analytics,  (7)  important IoT system capabilities such as IOT system reliability, robustness and security, and (8) IoT systems for XR applications. 

Expected Workload: The students will present research paper(s) in groups, and participate in discussion of presented papers. Furthermore, students will prepare project proposal, work on a semester-long project and present their results in a research paper format and via final presentation. Students can define their own project. Sample projects: design and development of multi-view  IoT  distribution algorithms; design and validation of evaluation methodology for diverse wireless multimedia IoT applications; comparative analysis of IoT data analytics algorithms;  ML algorithms for IoT data, development of IoT streaming protocols; performance monitoring algorithms and tool(s) for IoT systems; exploration of caching algorithms  in edge-cloud computing of IoT data, and others. 

Lecture Slides and Course discussion  are going to be available on discussion platform (campuswire) as we progress during the semester. 

Statement on CS CARES and CS Values and Code of Conduct

All members of the Illinois Computer Science department - faculty, staff, and students - are expected to adhere to the CS Values and Code of Conduct. The CS CARES Committee is available to serve as a resource to help people who are concerned about or experience a potential violation of the Code. If you experience such issues, please contact the CS CARES Committee. The instructors of this course are also available for issues related to this class.