EE59900A Mobile Computing, Sensing,
Learning, and Interactions



Course Info

Instructor: Prof. Sung-Ju Lee (profsj@kaist.ac.kr), N1 #306
TAs: Adiba Orzikulova, Hyunhee Cho, Doyun Park, Seokwon Yang
When: Mon/Wed 13:00-14:30
Where: N1 #111
Class website: mobility101.org
KLMS page: https://klms.kaist.ac.kr/course/view.php?id=171864
Class email: ee59900a@nmsl.kaist.ac.kr
Campuswire page: https://campuswire.com/p/G06180F78
Course registration: https://bit.ly/ee59900a-register
Office hours: By appointment

IMPORTANT NOTES

  • The class will be in-person by default.

    • USEFUL LINKS

  • Past projects: Visit here
  • Essay submission link: bit.ly/EE59900A-submit-essay

    • Class Overview

    In this advanced research course, we study the various aspects that enable mobile ubiquitous computing; network architectures, cloud computing, on-device machine learning, wearables, applications, security, privacy, and interaction methods. We will read and present seminal and state-of-the-art papers on each topic. Students will also design and demonstrate class projects in mobile applications or IoT services.

    Prerequisite

    • Programming Structure for Electrical Engineering (EE209 or equivalent); basic understanding of programming
    • General understanding of networking, systems, and machine learning
    • Passion and interest in mobility and research
    • Work ethic

    Grading Policy

    Quizzes 15% Quizzes may be announced or unannounced; includes open-ended questions and take home quizzes.
    Assignments 30% Reading assignments and essays + two programming assignments. Details will be guided within lectures.
    Project 25% A research term project that involves implementation.
    Presentations 20% Three project presentations and research paper presentations.
    Participation 10% This course would be successful only when it's interactive. Students are highly encouraged to ask questions, present their opinion, and lead discussions during classes and in Campuswire.

    Announcements

    • 9/2 Please complete the survey
    • 9/1 Welcome to EE59900A!


    Schedule

    Week Date Topic Required reading Submission
    1 9/1 Mon Introduction [slides] [video]
    9/3 Wed Challenges, Mobile sensing [slides] G. H. Forman and J. Zahorjan, "The Challenges of Mobile Computing," IEEE Computer, April 1994.
    M. Satyanarayanan, "Pervasive Computing: Vision and Challenges," IEEE Personal Communications, August 2001.
    E. Miluzzo, et al., "Darwin Phones: the Evolution of Sensing and Inference on Mobile Phones," ACM MobiSys 2010.
    2 9/8 Mon Wireless communications, LTE, WiFi [slides] [video] A. Gosh, et al., "LTE-Advanced: Next-Generation Wireless Broadband Technology," IEEE Wireless Communications, June 2010.
    W. Sun, et al., "Wi-Fi Could be Much More," IEEE Communications Magazine, November 2014.
    9/10 Wed Ad hoc & mesh networking [slides] [video] E. M. Royer and C.-K. Toh, "A Review of Current Routing Protocols for Ad Hoc Mobile Wireless Networks," IEEE Personal Communications, 1999
    D. S. J. De Couto, D. Aguayo, J. Bicket, and R. Morris, "A High-Throughput Path Metric for Multi-Hop Wireless Routing," ACM MobiCom 2003.
    J. Bicket, et al., "Architecture and Evaluation of an Unplanned 802.11b Mesh Network," ACM MobiCom 2005.
    3 9/15 Mon TCP over wireless, localization & energy efficiency intro [slides] [video] H. Balakrishnan, V. N. Padmanabhan, S. Seshan and R. H. Katz, "A Comparison of Mechanisms for Improving TCP Performance over Wireless Links," ACM SIGCOMM 1996.
    P. Bahl & V. N. Padnabhan "RADAR: an In-Building RF-based User Location and Tracking System," IEEE INFOCOM 2000
    N. Balasubramanian, et al., "Energy Consumption in Mobile Phones: A Measurement Study and Implications for Network Applications," ACM IMC 2009.
    9/17 Wed Mobile App Development Tutorial [slides] [video]
    4 9/22 Mon On-device Al & federated learning intro [slides] [video] H. B. McMahan, et al., "Communication-Efficient Learning of Deep Networks from Decentralized Data," AISTATS 2017.
    K. Bonawitz, "Towards Federated Learning at Scale: System Design," MLSys 2019.
    9/24 Wed Elevator pitch
    5 9/29 Mon Energy efficiency & Cloud [P1] A. Pathak, et al., "Fine Grained Energy Accounting on Smartphones with Eprof," ACM EuroSys 2012.
    [P2] E. Cuervo, et al., "MAUI: Making Smartphones Last Longer with Code Offload," ACM MobiSys 2010.
    10/1 Wed No Class (Armed Forces Day)
    6 10/6 Mon No Class (Chuseok)
    10/8 Wed No Class (Chuseok)
    7 10/13 Mon Digital Wellbeing [P3] A. Orzikulova, et al., "Time2Stop: Adaptive and Explainable Human-Al Loop for Smartphone Overuse Intervention," ACM CHI 2024.
    [P4] I. Song, et al., "The Typing Cure: Experiences with Large Language Model Chatbots for Mental Health Support," ACM CSCW 2025.
    10/15 Wed Half presentations
    8 10/20 Mon Midterm week No class
    10/22 Wed Midterm week No class
    Due: Take-Home Quiz (October 26th, Sunday 11:55 PM) Submit
    9 10/27 Mon Mobile Health [P5] S. Wu, et al., "CardioAl: A Multimodal Al-based System to Support Symptom Monitoring and Risk Prediction of Cancer Treatment-Induced Cardiotoxicity," ACM CHI 2025.
    [P6] Z. Englhardt, et al., "From Classification to Clinical Insights: Towards Analyzing and Reasoning About Mobile and Behavioral Health Data With Large Language Models," ACM UbiComp 2024.
    10/29 Wed Interaction Methods [P7] Y. Jiang, et al., "MouthIO: Fabricating Customizable Oral User Interfaces with Integrated Sensing and Actuation," ACM UIST 2024.
    [P8] Z. Xu, et al., "FingerGlass: Enhancing Smart Glasses Interaction via Fingerprint Sensing," ACM CHI 2025.
    10 11/3 Mon On-device ML tutorial [slides] [video]
    Due: Assignment 2 (November 17th, 11:59 PM) Submit
    11/5 Wed Mobile Computing at KAIST I (Author Presentation) [G1] H. Yoon, et al., "SelfReplay: Adapting Self-Supervised Sensory Models via Adaptive Meta-Task Replay," ACM SenSys 2025.
    [G2] M. Kim, et al., "NR-Surface: NextG-ready µW-reconfigurable mmWave Metasurface," USENIX NSDI 2024.
    [G3] J. Kim, et al., "Cross, Dwell, or Pinch: Designing and Evaluating Around-Device Selection Methods for Unmodified Smartwatches," ACM CHI 2025.
    11 11/10 Mon Wearables [P9] M. Kim, et al., "IRIS: Wireless Ring for Vision-based Home Interaction" ACM UIST 2024.
    [P10] Q. Xue, et al., "PPG Earring: Wireless Smart Earring for Heart Health Monitoring," ACM CHI 2025.
    11/12 Wed On-Device Al Framework [P11] R. David, et al., "TensorFlow Lite Micro: Embedded Machine Learning on TinyML Systems," MLSys 2021.
    [P12] . Lin, et al., "MCUNet: Tiny Deep Learning on loT Devices," NeurIPS 2020.
    12 11/17 Mon On-Device Al [P13] Y. D. Kwon, "TinyTrain: Resource-Aware Task-Adaptive Sparse Training of DNNs at the Data-Scarce Edge," ICML 2024.
    [P14] X. Li, et al., "FlexNN: Efficient and Adaptive DNN Inference on Memory-Constrained Edge Devices," ACM MobiCom 2024.
    11/19 Wed Federated Learning I [P15] C. Li, et al., "PyramidFL: A Fine-grained Client Selection Framework for Efficient Federated Learning," ACM MobiCom 2022.
    [P16] J. Geiping, et al., "Inverting Gradients - How Easy is it to Break Privacy in Federated Learning?" NeurIPS 2020.
    13 11/24 Mon Mobile Computing at KAIST II (Author Presentation) [G4] A. Orzikulova, et al., "BioQ: Towards Context-Aware Multi-Device Collaboration with Bio-cues," ACM SenSys 2025.
    [G5] J. Yun, et al., "PowDew: Detecting Counterfeit Powdered Food Products using a Commodity Smartphone," ACM MobiSys 2024.
    [G6] J. Lee, et al, "VeriSafe Agent: Safeguarding Mobile GUI Agent via Logic-based Action Verification," ACM MobiCom 2025.
    11/26 Wed Federated Learning II [P17] X. Ouyang, et al., "ADMarker: A Multi-Modal Federated Learning System for Monitoring Digital Biomarkers of Alzheimer's Disease," ACM MobiCom 2024.
    [P18] Z. Wang, et al., "FLORA: Federated Fine-Tuning Large Language Models with Heterogeneous Low-Rank Adaptations," NeurIPS 2024.
    14 12/1 Mon LLMs for On-Device [P19] E. J. Hu, et al., "LoRA: Low-Rank Adaptation of Large Language Models," ICLR 2022.
    [P20] Z. Liu, et al., "MobileLLM: Optimizing Sub-billion Parameter Language Models for On-Device Use Cases," ICML 2024.
    12/3 Wed LLMs for Mobile [P21] H. Wen, et al., "AutoDroid: LLM-powered Task Automation in Android," ACM MobiCom 2024.
    [P22] H. Wang, et al., "Never Start from Scratch: Expediting On-Device LLM Personalization via Explainable Model Selection," ACM MobiSys 2025.
    15 12/8 Mon Demo
    12/10 Wed Demo
    16 12/15 Mon Finals week No class
    12/17 Wed Finals week No class

    Class Policy

    Students are encouraged to interact with classmates, as well as the professor and the TAs, to discuss course material and assignment problems. In all your writing, including homework, essays, reports, and exams, use your own words, and acknowledge the source if you use someone else’s slides, quotes, figures, text, etc. Plagiarism and cheating are serious offenses and will be punished by failure on exams/assignments/course, and suspension or expulsion from the University.