|Quizzes||15%||Quizzes may be announced or unannounced; includes open-ended questions and take home quizzes.|
|Assignments||35%||Reading assignments and essays + three Android & Arduino programming assignments. Details will be guided within lectures.|
|Project||20%||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.|
|1||8/29||Mon||Course introduction [slides] [video]|
|8/31||Wed||Challenges in mobile sensing [slides] [video]||(1) G. H. Forman and J. Zahorjan, “The Challenges of Mobile Computing,” IEEE Computer, April 1994.
(2) M. Satyanarayanan, “Pervasive Computing: Vision and Challenges,” IEEE Personal Communications, August 2001.
(3) E. Miluzzo, et al., “Darwin Phones: the Evolution of Sensing and Inference on Mobile Phones,” ACM MobiSys 2010.
|2||9/5||Mon||Wireless communications, LTE, Wi-Fi [slides] [video]||(1) A. Gosh, et al., “LTE-Advanced: Next-Generation Wireless Broadband Technology,” IEEE Wireless Communications, June 2010.
(2) W. Sun, et al., “Wi-Fi Could be Much More,” IEEE Communications Magazine, November 2014.
|9/7||Wed||Ad hoc & mesh networking [slides] [video]||(1) E. M. Royer and C.-K. Toh, “A Review of Current Routing Protocols for Ad Hoc Mobile Wireless Networks,” IEEE Personal Communications, 1999.
(2) 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.
(3) J. Bicket, et al., “Architecture and Evaluation of an Unplanned 802.11b Mesh Network,” ACM MobiCom 2005.
|Due: Team formation|
|Out: Project Proposal Doc|
|Out: Paper Selection|
|9/13||Tue||Due: Paper Selection|
|9/14||Wed||TCP over wireless, localization & energy efficiency[slides]||(1) 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.
(2) P. Bahl & V. N. Padnabhan “RADAR: an In-Building RF-based User Location and Tracking System,” IEEE INFOCOM 2000.
(3) N. Balasubramanian, et al., “Energy Consumption in Mobile Phones: A Measurement Study and Implications for Network Applications,” ACM IMC 2009.
|4||9/19||Mon||Mobile Computing at KAIST I (author presentations)
|[GL1] Y. Yoon, et al., “SGToolkit: An Interactive Gesture Authoring Toolkit for Embodied Conversational Agents,” ACM UIST 2021.
[GL2] W. Kim, et al., "Hivemind: Social Control-and-Use of IoT towards Democratization of Public Spaces," ACM MobiSys 2021.
|Due: Paper essay (everyone; select 1)|
|Due: Google slides|
|5||9/26||Mon||Mobile App Development Tutorial [slides]|
|Out: Programming Assignment #1|
|Due: Project Proposal Doc|
|9/28||Wed||Energy efficiency & Cloud
|[P1] A. Pathak, et al., “Where is the Energy Spent Inside My App?: 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.
|6||10/3||Mon||National Foundation Day|
|10/5||Wed||Mobility & Life [Zoom] [slides1] [slides2]||[P3] R. Wang, et al., “Tracking Depression Dynamics in College Students Using Mobile Phone and Wearable Sensing,” ACM IMWUT (UbiComp) 2018.
[P4] C. Park, et al., “Online Mobile App Usage as an Indicator of Sleep Behavior and Job Performance,” ACM WWW 2021.
|Due: Programming Assignment #1|
|9||10/24||Mon||Arduino tutorial [slides]|
|Out: Programming Assignment #2|
|10/26||Wed||Digital Wellbeing [slides1] [slides2]||[P5] H. Cho, et al., “Reflect, not Regret: Understanding Regretful Smartphone Use with App Feature-Level Analysis,” ACM CSCW 2021.
[P6] X. Xu, et al., “TypeOut: Leveraging Just-in-Time Self-Affirmation for Smartphone Overuse Reduction,” ACM CHI 2022.
|10||10/31||Mon||Mobile Health [slides1] [slides2]||[P7] A. Curtis, et al., “HealthSense: Software-defined Mobile-based Clinical Trials,” ACM MobiCom 2019.
[P8] H. Zhang, et al., “PDLens: Smartphone Knows Drug Effectiveness among Parkinson’s via Daily-Life Activity Fusion,” ACM MobiCom 2020.
|11/2||Wed||Interaction Methods [slides1] [slides2]||[P9] P. Streli, et al., “TapType: Ten-finger Text Entry on Everyday Surfaces via Bayesian Inference,” ACM CHI 2022.
[P10] V. Shen, et al., “Mouth Haptics in VR Using a Headset Ultrasound Phased Array,” ACM CHI 2022.
|11||11/7||Mon||On-device ML tutorial [slides] [code]|
|Due: Programming Assignment #2||Out: Programming Assignment #3|
|11/9||Wed||Mobile Computing at KAIST II (author presentations) [slides1] [slides2]||[GL3] J. Shin, et al., “FedBalancer: Data and Pace Control for Efficient Federated Learning on Heterogeneous Clients,” ACM MobiSys 2022.
[GL4] J. Kim, et al., “HearMeOut: Detecting Voice Phishing Activities in Android,” ACM MobiSys 2022.
|12||11/14||Mon||Earables [slides1] [slides2]||[P11] I. Chatterjee, et al., “ClearBuds: Wireless Binaural Earbuds for Learning-Based Speech Enhancement,” ACM MobiSys 2022.
[P12] Y. Jin, et al., “EarHealth: An Earphone-based Acoustic Otoscope for Detection of Multiple Ear Diseases in Daily Life,” ACM MobiSys 2022.
|11/16||Wed||Federated Learning [slides1] [slides2]||[P13] F. Lai, et al., “Oort: Efficient Federated Learning via Guided Participant Selection,” USENIX OSDI 2021.
[P14] A. Li, et al., “FedMask: Joint Computation and Communication-Efficient Personalized Federated Learning via Heterogeneous Masking,” ACM SenSys 2021.
|13||11/21||Mon||Activity Recognition [slides1] [slides2]||[P15] T. Li, et al., “Making the Invisible Visible: Action Recognition Through Walls and Occlusions,” ICCV 2019.
[P16] C. Tang, et al., “SelfHAR: Improving Human Activity Recognition through Self-training with Unlabeled Data,” ACM IMWUT (UbiComp) 2021.
|Due: Programming Assignment #3|
|11/23||Wed||On-Device Training [slides1] [slides2]||[P17] X. Zeng, et al., “Mercury: Efficient On-Device Distributed DNN Training via Stochastic Importance Sampling,” ACM SenSys 2021.
[P18] I. Gim and J. Ko, “Memory-efficient DNN Training on Mobile Devices,” ACM MobiSys 2022.
|Out: Additional essay (replaces quiz)|
|14||11/28||Mon||Mobile Computing at KAIST III (author presentations) [slides1] [slides2]||[GL5] K. Bae, et al., “OmniScatter: Extreme Sensitivity mmWave Backscattering Using Commodity FMCW Radar,” ACM MobiSys 2022.
[GL6] S. Lee, et al., “A-Mash: Providing Single-App Illusion for Multi-App Use through User-centric UI Mashup," ACM MobiCom 2022.
|11/30||Wed||Undergraduate Admissions Interview|
|Due: Presentation slides||Submit|
|12/7||Wed||No class||Due: Additional essay (replaces quiz)||Submit|
|Due: Project report & deliverables [guideline] [template]||Submit|