Description: The objective of this course is to introduce the spectrum of research on the Internet of Things (IoT). The lectures cover a range of techniques in sensing, computing, communication, and wireless networking and connect them to various applications in analytics, localization, cyber-physical systems, mobile health, security, and wearables. This course is designed with multidisciplinary students in mind. Every topic begins from first principles and gradually ramps up to the system design and application, helping students to understand the state-of-the-art developments in this area and initiate research.
Topics:
- Gesture and motion sensing, activity detection, and physical analytics
- Ranging, beamforming, and source localization using acoustic and wireless signals
- Outdoor and indoor localization techniques
- Security and privacy: side channels, non-linearity, cross-domain attacks
- Autonomous systems (drones, self-driving vehicles)
- Augmented and virtual reality
- Wireless networks
- Battery-free sensing and networking
- Mobile health, human-centric computing
Prerequisite: The course is designed to be self-contained. It will cover introductory materials on mathematics and signal processing basics. Familiarity with the undergraduate level calculus, probability, linear algebra, and programming will be helpful.
Grading:
(1)
Homework (30%) - Micro assignments or programming after each module.
(2)
Class presentation (10%) - One 20 minutes presentation.
(3)
Midterm (30%) - One open book midterm exam based on the techniques discussed in the class.
(4)
Project (30%) - One semester-long project in a group of 2 students.
- No final examination.
- Bonus points will be capped per section (homework, class presentation, midterm, and project).
Textbook: N/A. Please follow lectures and reference materials mentioned in class.
Reference books:
(1)
Data-Driven Science and Engineering, J. Nathan Kutz and Steven L. Brunton
(2) Understanding Digital Signal Processing, Richard Lyons
