Today, computers are not just in boxes and data-centers; they are extensively interwoven with our physical life. They sense us, predict us, and empower us to do more, no matter when and where. In this light, this course is designed to help the students (1) broaden their views on computer science across our real life, (2) learn how fundamental computer science principles and technology are engineered to create our daily intelligent companions, and (3) develop interdisciplinary intellectual senses connecting computer science with other fields of study.

This course will cover the basic systems, technology, and real-life applications pertaining to mobile and ubiquitous computing. The technical pillars and applications include (but not limited to): human activity recognition & localization, smart homes, physical & mental healthcare, cyber-physical privacy, social computing & crowd computing in physical life, novel user interfaces, and mobile deep learning. The course will also include advanced issues to optimize mobile and ubiquitous computing systems’ latency and energy efficiency. Also, this course will refer to some historic research which influenced the birth of today’s commercial IoT devices the students may be familiar to, such as Nest learning thermostats, FitBits, and even commodity services such as WiFi localization, so that the students can learn from examples.

This course plans at least one guest lecture to be given by a mobile / wearable computing researcher from a renowned global research laboratory (or equivalent).

The course does NOT plan mid-term or final exams. Instead, the course will include a term-project, expecting the students to form teams and build their own ideas pertaining to mobile & ubiquitous computing.

This course is designed for senior students with knowledge on basic computer science principles and reasonable programming skills. Competent junior students should also fit. Prior experiences of mobile programming (e.g., Android, iOS, embedded platforms, etc.) will be a plus but not strictly required to take this course.

● Term Project 40%
- Proposal and development of your team’s own idea pertaining to mobile, ubiquitous, and/or wearable computing.
- Each team may consist of 1-2 students, depending on the project topic and the class size.
- Evaluation criteria: Novelty, usefulness, working demonstration, presentations, regular progress

● Weekly Critiques 30%
- Every week, pick one paper from the weekly paper stack, and write a single-paged critique, preferably in English.
- Evaluation criteria: Logic, depth, idea.

● Mobile Programming 20%
- Each student implements an Android app relevant to Mobile & Ubiquitous Computing, e.g., human activity recognizer.
- Evaluation criteria: End-to-end operation, performance, code readability, dataset integrity.

● Participation 10%
- In-class participation, e.g., asking questions, joining a discussion, presenting opinions.
- Evaluation criteria: Participation quantity and quality.

The compilation of papers to be covered in this course will be updated annually on PLMS before the semester opening. For the paper list covered in a previous year, please refer to the course website at:
https://www.his-lab.org/course-mobile-ubicomp
Every year, the list of papers will undergo partial updates.

Week 1
- Course Introduction
- Philosophical Overview of Mobile & Ubiquitous Computing

Week 2
- Human Activity Recognition
- Emotion, Mood, Empathy

Week 3
- Android Programming Part I: Android Overview
- Smarter Home

Week 4
- Android Programming Part II: Live Coding Showcase
- Cyber-Physical Privacy

Week 5
- [Term Project] Proposal presentations

Week 6
- Android Programming Part III: Programming Android in Kotlin
No Tuesday class due to the university policy

Week 7
- Crowdsensing
- Socio-Physical Computing

Week 8
Sensing and Intervention for Sleep
Sensing for Healthy Food and Body

Week 9
- Indoor Localization I. Continuous Positioning & Tracking
- Indoor Localization II. Discrete High-level Primitives

Week 10
- Special Invited Lecture by a Ubiquitous Computing Researcher in Global Industry

Week 11
- [Term Project] Mid-term progress presentations

Week 12
Object Localization I. Positioning
Object Localization II. Ranging

Week 13
- Optimizing Continuous Sensing
- Mobile-Cloud Collaboration

Week 14
- Re-thinking Mobile User Interface
- Don’t be a Slave! Towards Healthier Mobile Usage

Week 15
- Mobile Deep Learning I. Optimizing Systems and Pipelines
- Mobile Deep Learning II. Novel Applications

Week 16
- [Term Project] Final presentations
- Final term-project report due

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