CS 6998: Topics in Mobile X is an upper-level course on mobile computing and ubiquitous systems, covering a broad range of advanced and interdisciplinary topics in mobile computing, networking, and applications. All these topics center on unique challenges faced on bringing computation, networking, and applications to the mobile computing platform -- a platform that is constrained in form factor, energy, and computation power, with examples such as smartphones, smart watches or wrist bands, smart glasses, and more. Example topics include mobile communication and networking, mobile sensing, mobile human-to-computer interaction (HCI), mobile learning/AI, and mobile security.
For each topic, we will study both conventional perspectives and recent research trends. Students will learn key principles in mobile computing research, understand the state of the art in this research area, and gain experiences of carrying out original research through class projects. The end goal is to generate publishable results by the end of the term. In addition, students will practice their skills in paper reading/writing and public presentation in the form of weekly research essays, class discussion, paper critics, project report and presentation.
As a research-oriented course, this course is based on research papers in top-tier conferences. No particular textbooks are required, but following textbooks are good references for students to refresh networking background and better understand papers.
- Theodore Rappaport. Wireless Communications: Principles and Practice.
- William Stallings. Wireless Communications and Networks.
- Pei Zheng, Larry L. Peterson, Bruce S. Davie, and Adrian Farrel. Wireless Networking Complete.
- James Kurose and Keith Ross. Computer Networking: A Top-Down Approach.
- Larry Peterson and Bruce Davie. Computer Networks: A Systems Approach.
- Andrew S. Tanenbaum. Computer Networks.
Because this is a high-level course, we assume that students already have a solid understanding on the basics of some areas including networking, communication, and machine learning. There are multiple ways of demonstrating a networking and machine learning background, including taking undergraduate networking class, machine learning class, or equivalent classes, and experiences of working on related projects. Course project is an important part of this class. Students must have good programming skills and project experiences.
Class presentation: 20%
Class participation: 5%