CS 537 Fall 2023 - Advanced Topics in IoT (Internet of Things)
Class Location: 1302 Siebel Center for on-campus students
Class Time: Tuesdays and Thursdays 12:30:00-1:45pm
Office Hours (Instructor): Tuesdays and Thursdays: 2:00-3:00 pm (for students on campus in person 3104 Siebel Center)
Office Hours (Instructor): Tuesdays and Thursdays: 3:00-4:00 pm (for online students - zoom will be posted on campuswire)
Instructor: Prof. Klara Nahrstedt (firstname.lastname@example.org)
Teaching Assistant: No TA
Reading List: reading list will be posted by September 25
Course Details: Introduction
Goal: Advanced topics in Internet of Things (IoT) 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, multi-modal IoT systems, 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 multi-modal 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 360 video cameras, 1D audio, IMU and other 1D IoT devices (e.g., temperature, humidity), (2) advanced compression techniques for 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), work individually on take-home midterm exam, and participate in discussion of presented papers. Furthermore, students will 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 multi-modal IoT applications; comparative analysis of IoT data analytics algorithms; 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 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.