Department of Computer Science and Technology

Course pages 2018–19

Advanced topics in mobile and sensor systems and data modelling

Lecture Material

2nd November 2018 (1h): Introduction.

9th November 2017: MobileOS, Resource, Privacy and Energy.

  1. Eduardo Cuervo, Alec Wolman, Landon P. Cox, Kiron Lebeck, Ali Razeen, Stefan Saroiu, and Madanlal Musuvathi. 2015. Kahawai: High-Quality Mobile Gaming Using GPU Offload. In Proceedings of the 13th Annual International Conference on Mobile Systems, Applications, and Services (MobiSys '15). PDF.
  2. Chulhong Min, Youngki Lee, Chungkuk Yoo, Seungwoo Kang, Sangwon Choi, Pillsoon Park, Inseok Hwang, Younghyun Ju, Seungpyo Choi, and Junehwa Song. 2015. PowerForecaster: Predicting Smartphone Power Impact of Continuous Sensing Applications at Pre-installation Time. In Proceedings of the 13th ACM Conference on Embedded Networked Sensor Systems (SenSys '15). PDF.
  3. Raul Herbster, Scott DellaTorre, Peter Druschel, and Bobby Bhattacharjee. 2016. Privacy Capsules: Preventing Information Leaks by Mobile Apps. In Proceedings of the 14th Annual International Conference on Mobile Systems, Applications, and Services (MobiSys '16). PDF.

16th November 2018: Activity Recognition with Machine Learning and On Device Learning

  1. Nils Y. Hammerla, Shane Halloran, and Thomas Ploetz. 2016. Deep, convolutional, and recurrent models for human activity recognition using wearables. In Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence (IJCAI'16), Gerhard Brewka (Ed.). AAAI Press 1533-1540. PDF.
  2. Seungyeop Han, Haichen Shen, Matthai Philipose, Sharad Agarwal, Alec Wolman, and Arvind Krishnamurthy. 2016. MCDNN: An Approximation-Based Execution Framework for Deep Stream Processing Under Resource Constraints. In Proceedings of the 14th Annual International Conference on Mobile Systems, Applications, and Services (MobiSys '16). PDF.
  3. Shuochao Yao, Yiran Zhao, Huajie Shao, Aston Zhang, Chao Zhang, Shen Li, and Tarek Abdelzaher. 2018. RDeepSense: Reliable Deep Mobile Computing Models with Uncertainty Estimations. Proc. ACM Interact. Mob. Wearable Ubiquitous Technol. Volume 1 Issue 4. 2018 PDF.

23rd November 2018: Mobile Sensing

  1. Tauhidur Rahman, Alexander T. Adams, Mi Zhang, Erin Cherry, Bobby Zhou, Huaishu Peng, and Tanzeem Choudhury. 2014. BodyBeat: a mobile system for sensing non-speech body sounds. In Proceedings of the 12th annual international conference on Mobile systems, applications, and services (MobiSys '14). PDF.
  2. Tianxing Li, Qiang Liu, and Xia Zhou. 2016. Practical Human Sensing in the Light. In Proceedings of the 14th Annual International Conference on Mobile Systems, Applications, and Services (MobiSys '16) PDF.
  3. Sheng Shen, He Wang, and Romit Roy Choudhury. 2016. I am a Smartwatch and I can Track my User's Arm. In Proceedings of the 14th Annual International Conference on Mobile Systems, Applications, and Services (MobiSys '16). PDF.

30th November 2018: Backscatter Communication and Battery Free Devices

  1. Pengyu Zhang, Mohammad Rostami, Pan Hu, and Deepak Ganesan, Enabling Practical Backscatter Communication for On-body Sensors, Proceedings of ACM SIGCOMM 2016. PDF.
  2. Vamsi Talla, Bryce Kellogg, Shyamnath Gollakota, and Joshua R. Smith. 2017. Battery-Free Cellphone. Proc. ACM Interact. Mob. Wearable Ubiquitous Technol. 1, 2. PDF.
  3. Yunfei Ma, Nicholas Selby, and Fadel Adib. 2017. Drone Relays for Battery-Free Networks. In Proceedings of the Conference of the ACM Special Interest Group on Data Communication (SIGCOMM '17). PDF.

21st January 2019: Mobile Health.

  1. Rui Wang, Weichen Wang, Alex daSilva, Jeremy F. Huckins, William M. Kelley, Todd F. Heatherton, and Andrew T. Campbell. 2018. Tracking Depression Dynamics in College Students Using Mobile Phone and Wearable Sensing. Proc. ACM Interact. Mob. Wearable Ubiquitous Technol. 2, 1, PDF.
  2. Rajalakshmi Nandakumar, Shyamnath Gollakota, and Nathaniel Watson. 2015. Contactless Sleep Apnea Detection on Smartphones. In Proceedings of the 13th Annual International Conference on Mobile Systems, Applications, and Services (MobiSys '15). PDF.
  3. Mingmin Zhao, Shichao Yue, Dina Katabi, Tommi Jaakkola, Matt Bianchi. Learning Sleep Stages from Radio Signals: A Conditional Adversarial Architecture. International Conference on Machine Learning (ICML17). PDF.

28th January 2018:Urban Mobility Modelling.

  1. Hua Wei, Guanjie Zheng, Huaxiu Yao, Zhenhui Li. IntelliLight: A Reinforcement Learning Approach for Intelligent Traffic Light Control. In Proceedings of ACM Conference on Knowledge Discovery and Data Mining. 2018. PDF
  2. Suiming Guo, Chao Chen, Jingyuan Wang, Yaxiao Liu, Ke Xu, Daqing Zhang, and Dah Ming Chiu. 2018. A Simple but Quantifiable Approach to Dynamic Price Prediction in Ride-on-demand Services Leveraging Multi-source Urban Data. Proc. ACM Interact. Mob. Wearable Ubiquitous Technol. 2018. PDF
  3. Zidong Yang, Ji Hu, Yuanchao Shu, Peng Cheng, Jiming Chen, and Thomas Moscibroda. 2016. Mobility Modeling and Prediction in Bike-Sharing Systems. In Proceedings of the 14th Annual International Conference on Mobile Systems, Applications, and Services (MobiSys '16). PDF

11th February: Applications of Mobile Data Analytics.

  1. Merkebe Getachew Demissie, Santi Phithakkitnukoon, Titipat Sukhvibu, Francisco Antunes, Rui Gomes, Carlos Bento. 2016. Inferring passenger travel demand to improve urban mobility in developing countries using cell phone data: a case study of Senegal. IEEE Transactions on Intelligent Transportation Systems, 17(9), pp.2466-2478. PDF
  2. Ciro Cattuto, Wouter Van den Broeck, Alain Barrat, Vittoria Colizza, Jean-François Pinton, Alessandro Vespignani. Dynamics of Person-to-Person Interactions from Distributed RFID Sensor Networks. Paper Link
  3. Tim Althoff, Rok Sosic, Jennifer L. Hicks, Abby C. King, Scott L. Delp, Jure Leskovec. 2017. Large-scale physical activity data reveal worldwide activity inequality. Nature, 547(7663), p.336. PDF and Supplementary
  4. Cristina Kadar, Irena Pletikosa. Mining large-scale human mobility data for long-term crime prediction. EPJ Data Science20187:26 Paper Link