CS8803 - Mobile Computing and IoT

This course will teach a variety of ideas, concepts, techniques, and algorithms, that are all crucial to understanding and developing mobile systems and applications. The course will begin from first principles and ramp up to real-world systems and technologies. Keywords related to this course includes: wireless sensing, localization, GPS, drones, sensors, motion tracking, acoustics, location privacy, etc.

We will start from absolute basics, and cover important modules from linear algebra and data/signal processing. We will assume that you do not recall anything from prior courses (even if you have taken them). We will discuss the crossover of ideas from these modules to wireless communication and mobile computing. The course is designed with CS students in mind, particularly those inclined towards systems and networking.

Understanding GPS, understanding why indoor positioning still not available ... Location fingerprinting (WiFi, magnetic, BLE), crowd-sourcing, mapping. Unsupervised data-driven learning, clustering, sensor fusion, filtering, simultaneous localization and mapping (SLAM).

Understanding IMU (accelerometer, gyroscope, compass) Can a smartwatch track human gestures and activities? Can embedded IMUs track the motion of a fast-moving baseball? Motion models and filtering techniques

Next generation of wearable devices: finger rings, ear-buds, smart clothing. Rings: Vibration and ultrasound, receiving vibrations (with IMU and microphone), body-channels. Hearables and earables: noise cancellation, bone conduction, motion to speech recovery, binaural sounds, energy optimization (wake-on-speech)

Core challenges in autonomous systems: sensing, computing, communications + actuation. Robotic wireless networks, 5G networks, cell-tower on flying drones, ray-tracing, channel optimization. Cars: LIDAR, RADAR, and vision, sensor fusion, relative map creation.

Why personal, always-ON devices are a major challenge in security and privacy Side channel attacks, inference algorithms, hardware loopholes, sensor data leaks. Case studies: location privacy, password typing, Alexa attacks, IMU fingerprints, acoustic drone attack, clock leaks, etc.

The initial course topics are taught in class. For later topics students are expected to read research papers about the topic, and answer a short quiz on Canvas. We will be discussing those papers, and students are expected to participate in the class with comments and thoughts about the work. The course will have 2 homeworks, 2 graded programming assignments (PAs), 1 midterm exam, and a final project.

Homework: 10%
Paper Reviews: 10%
Midterm: 15%

PA0 + PA1 + PA2: 0% + 5% + 10%
Project: 50%