CAS765: Wireless Networking & Mobile Computing

Instructors: Rong Zheng

Email: rzheng

Lectures: M 9am - 12pm,

Office Hours: Tue 4:30pm - 6:30pm, ITB 121

Class web site: http://www.cas.mcmaster.ca/~rzheng/course/CAS765fa15/syllabus.html

Prerequisites: Matlab, Probability (understanding of Bayesian rules, total probability etc.)

Synopsis:

With the prevalence of wireless technologies and broadband access, untethered mobile and wearable devices have become an essential part of everyday life. This class aims to cover data acquisition, sensor signal processing, and machine learning techniques relevant to mobile computing as well as state-of-the-art research in this area.

Reference books & materials: (not required)

• Christopher Bishop, "Pattern Recognition and Machine Learning", Springer, 2007
• Greg Milette, Adam Stroud, "Professional Android Sensor Programming", 2012
• Android Programming: The Big Nerd Ranch Guide

Tentative Course schedule:

Homework

1. WiFi-based trilateration
2. Step counting and phone attitude estimation
3. Particular filter with IMU for indoor localization
4. Room tag recognition

Suggested Final Project Topics (1 or 2 students per group)

1. Robust step counting: the peak detection based step counting method introduced in the class is sensitive to the choice of filter parameters. In reality, gait patterns are person dependent. This is especially true for people with movement difficulties. The project aims to develop a robust step counting method that requires zero-configuration and is adaptive to individual's gaits. (See [JSPJ09] for robust features that can be used for step counting.)
2. Indoor-or-Outdoor detection: one issue in indoor positioning is to determine whether a person is inside a building or outside. For example, a person is inside a building, one may swtich from using GPS to a different localization solution that works better indoor both in accuracy as well as power consumption. Thus, a naive solution that uses GPS directly will consume to much power. (See [ZZLLS12][RKSM14] on the reading list and some follow-up work).
3. Reliable floor level detection: detecting which floor a person is at is another problem in indoor localization. Several approaches have been investigated in literatures, namely, detecting stair asending/descending and use of elevators, use of barometer sensors for air conditioned buildings.
4. Integration of visual cues in indoor localization: visual cues such as arrows, signates, labels are abundant in indoor spaces. Extracting these visual cues using smart glasses and incorporate them in providing better localization solutions is promising(whether as part of observation models, or to label indoor space, ...).
5. Gamification of indoor localization campaign: Fingerprinting using WiFi, magnetic fields or light intensity is shown to be useful in indoor localization. However, extensive site survey to collect fingerprints is labor intensive. Gamification on the other hand has the potential to make the process fun and attractive. The goal of this project is to design a game to attract users to collect fingerprint measurements with location tags. An alterative topic would be to design games for floor map construction or correcting existing floor maps.
6. WiFi + IMU SLAM In practice, location of WiFi APs are often not known. This makes it challenging to apply triagulation based localization solutions. The goal of this project is to design and implement a solution that combines WiFi readings (as part of observations) and IMU sensor readings (as part of motion model) for SLAM for the estimation of the locations of WiFi APs and user locations (See [JMQSTA11]).
7. Comparative study of power profiling/accounting approaches on mobile devices: Lightweight solutions to profiling the power usage of mobile applications are essential to drive decisions for mobile offloading and computation partitioning across different platforms (e.g., wearables, phones and cloud). This project aims to provide a quantative study of existing methods in terms of overhead and accuracy. See [DZ11], [PHZ12], [XLLZ13].

Reading list

• Indoor localization
1. [JSPJ09] A. Jim´enez, F. Seco, C. Prieto, and J. Guevara. A comparison of pedestrian dead-reckoning algorithms using a low-cost MEMS IMU. In Intelligent Signal Processing, IEEE International Symposium on, pages 37–42, 2009.
2. [XZH15] Qiang Xu, Rong Zheng, Steve Hranilovic, “IDyLL: Indoor Localization using Inertial and Light Sensors on Smartphones”, UbiComp’15
3. [ZZLLS12] Pengfei Zhou, Yuanqing Zheng, Zhenjiang Li, Mo Li, and Guobin Shen, “IODetector: A Generic Service for Indoor Outdoor Detection, SenSys’12
4. [RKSM14] Valentin Radu, Panagiota Katsikouli, Rik Sarkar, and Mahesh K. Marina, Semi-Supervised Learning Approach for Robust Indoor-Outdoor Detection with Smartphones, SenSys'14
5. [JMQSTA11] Joseph Huang, David Millman, Morgan Quigley, David Stavens, Sebastian Thrun and Alok Aggarwal Efficient, Generalized Indoor WiFi GraphSLAM, ICRA11
• Power profiling and accounting
1. [DZ11] Mian Dong and Lin Zhong, “Self-Constructive High-Rate System Energy Modeling for Battery-Powered Mobile Systems”, MobiSys’11
2. [PHZ12] Abhinav Pathak , Y. Charlie Hu, Ming Zhang, “Where is the energy spent inside my app? Fine Grained Energy Accounting on Smartphones with Eprof”, EuroSys’12
3. [XLLZ13] Fengyuan Xu, Yunxin Liu, Qun Li, Yongguang Zhang, “V-edge: Fast Self-constructive Power Modeling of Smartphones Based on Battery Voltage Dynamics NSDI’13
• Social implications of mobile computing
1. [C15] Delphine Christin, Privacy in mobile participatory sensing: Current trends and future challenges, The Journal of Systems and Software 000 (2015) 1–12
2. [NRFK15] Eleni Nasiopoulos1, Evan F. Risko, Tom Foulsham and Alan Kingstone, “Wearable computing: Will it make people prosocial?, British Journal of Psychology, 2015
3. [KG15] Kirkham, R.; Greenhalgh, C., "Social Access vs. Privacy in Wearable Computing: A Case Study of Autism," Pervasive Computing, IEEE, vol.14, no.1, pp.26,33, Jan.-Mar. 2015
4. [H14] Roberto Hoyle, et al, Privacy Behaviors of Lifeloggers using Wearable Cameras, UbiComp’14
• Incentive design and gamification
1. [YXFT12] Yang, D., Xue, G., Fang, X., & Tang, J. (2012). Crowdsourcing to Smartphones : Incentive Mechanism Design for Mobile Phone Sensing. MobiCom’12
2. [DM15] Dergousoff, K., Mandryk, R.L. 2015. Mobile Gamification for Crowdsourcing Data Collection: Leveraging the Freemium Model. In CHI'15 Workshop
3. [GHH15] Lin Gao, Fen Hou, and Jianwei Huang, “Providing Long-Term Participation Incentive in Participatory Sensing”, INFOCOM’15
4. [TCSOC12] Andrei Tamilin, Iacopo Carreras, Emmanuel Ssebaggala, Alfonse Opira, Nicola Conci, "Context-Aware Mobile Crowdsourcing", Ubicomp'12
5. [UTAY14] Ueyama, Y.; Tamai, M.; Arakawa, Y.; Yasumoto, K., "Gamification-based incentive mechanism for participatory sensing," in Pervasive Computing and Communications Workshops (PERCOM Workshops)