CS 537 Fall 2021 - Advanced Topics in IoT (Internet of Things)
Location: Online (zoom link will be advertised to all registered students via email)
Class Time: Tuesdays and Thursdays 2:00-3:15pm
Office Hours (Instructor): Tuesdays and Thursdays: 4:00-5:00 pm (in person 3104 Siebel Center or via zoom)
Instructor: Prof. Klara Nahrstedt (email@example.com)
Teaching Assistant: Ayush Sarkar (firstname.lastname@example.org)
Reading List: Reading-List-cs537-Fall2021
Course Details: Slides-About-Course-Details
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 compression, streaming and caching of IoT data, IoT analytics and feature learning, IoT-edge-cloud computing infrastructures, resource optimization for multi-modal IoT systems, applications and human perception of IoT. 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, 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, and other 1D IoT devices (e.g., temperature, humidity), (2) advanced compression techniques for IoT streams, including H.264/H.265, MPEG4/HVEC, MP3, (3) Machine Learning Techniques for IoT Data Analytics, (4) IoT network and transport protocols such as DASH, Zigbee, (5) edge-cloud computing systems for IoT Data Analytics, and (6) subjective and objective Quality of Experience (QoE) evaluation methods for next generation multi-modal IoT applications.
Expected Workload: The students will present 2 research paper, review three research papers (other than the presented papers), work individually on take-home midterm exam, and participate in zoom-discussion of presented papers. Furthermore, students will work on a semester-long project and present their results in a research paper and final presentation. Students can define their own project. Sample projects: design and development of multi-view IoT distribution algorithms, design and validation of QoE evaluation methodology for diverse wireless multi-modal IoT applications; development of DASH Adaptive control algorithms, QoS analytics algorithms and tool(s) for IoT systems, caching in edge-cloud computing of IoT data, and others.
Lecture Slides and recordings of lectures are available on compass2g, Paper Reviews and Class Discussion are available on campuswire.
Note for Illinois students: If you are not registered for the class and would like to attend the first few classes to make the final decision about your courses' registration, please, contact the instructor to share the zoom link with you.