Topical research themes 1 - Mobile Sensing

The weekly schedule indicates when will each of these topics be covered in class. However, you are encouraged to read the articles before the class:

• [Wei91] Mark Weiser. The Computer for the 21th Century. Scientific American. September 1991.
  
• [Wei93] Mark Weiser. Some Computer Issues in Ubiquitous Computing. Communications of the ACM.Vol. 36. Issue 7. July 1993.
• [LBG+16] Lane, Nicholas D., et al. "Deepx: A software accelerator for low-power deep learning inference on mobile devices." IEEE IPSN, 2016.
• [LBM+17] Lane, Nicholas D., et al. "Squeezing deep learning into mobile and embedded devices." IEEE Pervasive Computing 16.3 (2017): 82-88.
• [WWD+18] Wang, Rui, et al. "Tracking depression dynamics in college students using mobile phone and wearable sensing." ACM IMWUT (2018): 43.
  
• [WCC+14] Wang, Rui, et al. "StudentLife: assessing mental health, academic performance and behavioral trends of college students using smartphones." ACM UbiComp 2014.
• [LRC+12] Lu, Hong, et al. "Stresssense: Detecting stress in unconstrained acoustic environments using smartphones." ACM UbiComp, 2012.
• [RMM+10] Rachuri, K., et al. "EmotionSense: a mobile phones based adaptive platform for experimental social psychology research." ACM UbiComp, 2010.
• [Sat01] M. Satyanarayanan. Pervasive Computing: Vision and Challenges. IEEE Personal Communications. Vol. 8 Issue 4.August 2001.
• [MPV+16] Mehrotra, Abhinav, et al. "My phone and me: understanding people's receptivity to mobile notifications." ACM CHI, 2016.
• [CHS+18] Anderson, Christoph, et al. "A survey of attention management systems in ubiquitous computing environments." ACM IMWUT 2.2 (2018): 58.
• [VSK+15] Vallina-Rodriguez, Narseo, et al. "Beyond the radio: Illuminating the higher layers of mobile networks." ACM MobiSys 2015.
• [LPY+16] Li, Yuanjie, et al. "Mobileinsight: Extracting and analyzing cellular network information on smartphones." ACM MobiCom, 2016.
• [TLL+18] Tu, Zhen, et al. "Your apps give you away: distinguishing mobile users by their app usage fingerprints." ACM IMWUT (2018): 138.
• [MHV+13] De Montjoye, Yves-Alexandre, et al. "Unique in the crowd: The privacy bounds of human mobility." Scientific reports 3 (2013): 1376.
  
• [CLM+08] Campbell, Andrew T., et al. "The rise of people-centric sensing." Internet Computing, IEEE 12.4 (2008): 12-21.
• [LML+10] Lane, Nicholas D., et al. "A survey of mobile phone sensing." Communications Magazine, IEEE 48.9 (2010): 140-150.
• [PM15] Pejovic, Veljko, and Mirco Musolesi. "Anticipatory mobile computing: A survey of the state of the art and  research challenges." ACM Computing Surveys (CSUR) 47.3 (2015): 47.
• [G15] Google Material Design Guidelines www.google.com/design/spec/material-design/introduction.html
• [LPA+09] Lazer et al., "Computational Social Science", Science. 2009 Feb 6; 323(5915): 721–723.
  
• [MLF+08] Miluzzo et al., "Sensing meets mobile social networks: the design, implementation and evaluation of the cenceme application", SenSys'08, 2008.
• [API+11] Aharony et al., "The Social fMRI: Measuring, Understanding, and Designing Social Mechanisms in the Real World", UbiComp'11, 2011.
• [EP06] Eagle and Pentland, "Reality Mining: Sensing Complex Social Systems. Personal and Ubiquitous Computing" 10, 4 (March 2006), 255–268.
• [AVG+13] Aucinas et al., "Staying Online While Mobile: The Hidden Costs", CoNEXT'13, 2013.
• [RWC+15] Riccato et al., "Estimating population density distribution from network-based mobile phone data", JRC Tech Report, 2015.
• [RMM+11] Rachuri et al., "Sociablesense: exploring the trade-offs of adaptive sampling and computation offloading for social sensing", MobiCom'11, 2011.
• [CTS+14] Chon et al., "SmartDC: Mobility Prediction-Based Adaptive Duty Cycling for Everyday Location Monitoring", IEEE Transactions On Mobile Computing, 13 (3), 2014.
• [PM14] Pejovic and Musolesi, "InterruptMe: Designing intelligent prompting mechanisms for pervasive applications", UbiComp'14, 2014.
• [Kay15] Janet Kayfetz, Academic Writing, Columbia University Computer Science course, 2015.
• [CMT+08] Consolovo et al., "Activity Sensing in the Wild: A Field Trial of UbiFit Garden", CHI'08, 2008.
• [CM15] Canzian and Musolesi, "Trajectories of Depression: Unobtrusive Monitoring of Depressive States by means of Smartphone Mobility Traces Analysis", Ubicomp'15, 2015.
• [NGW15] Nandakumar et al., "Contactless Sleep Apnea Detection on Smartphones", MobiSys'15, 2015.
• [APJ+12] Anand et al., "VillageCell: Cost Effective Cellular Connectivity in Rural Areas", ICTD'12, 2012.
• [CBA12] Chaudhri et al., "Pervasive Computing Technologies to Monitor Vaccine Cold Chains in Developing Countries", ACM DEV'12, 2012.
• [VSL+13] Vitos et al., "Making local knowledge matter Supporting non-literate people to monitor poaching in Congo", ACM DEV'13, 2013.
• [CCH+15] Chen et al., "Invisible Sensing of Vehicle Steering with Smartphones", MobiSys'15, 2015.
• [RNB+11] Ravindranath et al., "Improving wireless network performance using sensor hints", USENIX NSDI'11, 2011.
  
• [AAB16] Alberdi et al. "Towards an automatic early stress recognition system for office environments based on multimodal measurements: A review." Journal of biomedical informatics 59 (2016): 49-75.
• [GLG+17] Gjoreski et al. "Monitoring stress with a wrist device using context." Journal of biomedical informatics 73 (2017): 159-170.
• [AKK+14] Adib et al. "3D tracking via body radio reflections." 11th {USENIX} Symposium on Networked Systems Design and Implementation ({NSDI} 14). 2014.
  
• [PVC+18] Pinder et al. "Digital Behaviour Change Interventions to Break and Form Habits." ACM Transactions on Computer-Human Interaction (TOCHI) 25 (3), 15
• [ZPH19] Zhang, C., Patras, P., & Haddadi, H. (2019). Deep learning in mobile and wireless networking: A survey. IEEE Communications Surveys & Tutorials, 21(3), 2224-2287.
• [NTA+18] Nweke, H. F., Teh, Y. W., Al-Garadi, M. A., & Alo, U. R. (2018). Deep learning algorithms for human activity recognition using mobile and wearable sensor networks: State of the art and research challenges. Expert Systems with Applications, 105, 233-261.
• [LZL+17] Li, X., Zhang, D., Lv, Q., Xiong, J., Li, S., Zhang, Y., & Mei, H. (2017). IndoTrack: Device-free indoor human tracking with commodity Wi-Fi. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, 1(3), 1-22.
• [Agg18] Aggarwal, C. Autoencoders, 2018.