15-821/18-843: Mobile and Pervasive Computing

Fall 2016

Course Web page at http://www.cs.cmu.edu/~15-821

Demo Day Videos and Posters (December 6, 2016)



1a.  Wearable Cognitive Assistance for AED Devices (device operation)

Mentors: Zhao Siyan and Junjue Wang
Students:
Hongkun Leng, Haodong Liu, Yuqi Liu (Poster, Video)

Original description
Using an Automatic External Defibrillator (AED) is a time-critical task. If done correctly, it can save many lives. However, there is currently no easy way to guide a novice user through the procedure without on-site support from trained personnel. But Wearable Cognitive Assistants can change this.  With wearable devices like Google Glass, it is possible to continuously capture what the user is looking at. An assistance system for tasks like using an AED, can be built on top of this such that 1) The system tries to understand the user’s progress with computer vision techniques; 2) The cognitive assistant provides step-by- step guidance to the user; and 3) The system gives user feedback based on progress. The video captured by Glass will be streamed to a Cloudlet and processed in real-time.

This project requires some computer vision background (if you do not know anything, you can learn along the way). We have a Gabriel platform that takes care of communications between mobile clients and cloudlets. You'll mainly be using python to program cognitive processing for the AED. Small customization for Android client may also be needed. It also involves Android programming (on Google Glass). The real-time video transmission part is already built and open-sourced.

1b.  Wearable Cognitive Assistance for AED Devices (pad placement)

Mentor: Zhao Siyan and Junjue Wang
Students:  Jineet Doshi, Toby Li, Rui Silva  (Poster, Video)
Original description same as 1a

2. Privacy Mediator for Audio Data

Mentor: Junjue Wang
Students: 
Ankit Jain, Rajat Pandey, Ayushi Singh (Poster, Video)
Original description
Consumer acceptance of IoT deployment is shadowed by privacy concerns. Users lack control over raw data that is directly streamed from sensors to the cloud. Current cloud-based IoT architecture does not have a clean method to let users filter and retain their sensitive data. A vision of using cloudlets to preserve privacy has been proposed. Imagine the following scenario in the future. You're in a room talking with friends. Microphones in your smartphones and security cameras are recording your conversation and sending them to the cloud for analysis. Although you rely on cloud audio analysis services to automatically take note and set up reminders from conversations, you want to exclude sensitive information, for example, salaries and social security numbers, from raw audio data that is uploaded. You leverage cloudlets as privacy mediators to preserve your privacy. Audio data is transmitted to a trusted cloudlet before going into the cloud. On the cloudlet, audio analysis is performed in real-time to filter out sensitive information. For example, the cloudlet changes the audio containing the email password you said during a conversation into beeping. 

In this project, you'll build such a privacy mediator for audio data using cloudlets. We already have a framework named Gabriel that takes care of data transmissions between mobile clients and cloudlets. You'll focus on audio data analysis on cloudlets. There are a few widely-used open-source automatic speech recognition frameworks you can leverage (CMU Sphinx, Kaldi, etc).

4.  Hub for Internet of Things

Mentor:  Jason Hong
Students: Ken Ling, Lehao Sun, Mengjin Yan (PosterVideo)

Original description
One likely deployment model for Internet of Things is to have centralized hubs that can offer devices network connections, check for firmware or software updates, and monitor traffic for anomalous behaviors. This project seeks to develop new ways of adding new devices to this hub in a simple and secure manner, as well as offering new kinds of services, such as linking different devices together or doing simple kinds of end-user programming.

7. Interactive Rehabilitation Device

Mentor: Zhuo Chen
Students:
  Eric  Markvicka, Tianshi Li
(Poster, Video)

Original description
This project will use a wearable biomonitoring device that is adhesively mounted to the hand to estimate position and monitor the user’s heart rate and blood oxygen saturation. The device will be used for stroke rehabilitation to understand when the hand becomes impeded due to muscle stiffening. Specifically, the project will aim to answer the following questions: 1) What is the position of the hand when muscle stiffening occurs, 2) what motion of the hand initiated muscle stiffening, and 3) how was the muscle stiffening alleviated. In addition, the user’s heart rate and blood oxygen saturation will be monitored at the fingertip. For this project, data from the wearable device will be transmitted to a smart phone via Bluetooth link. The cloudlet infrastructure will be used to off-load data processing and storage. An interactive user interface will display relevant biomonitoring signals and suggests ways to prevent or alleviate muscle stiffening.

9. Combining a Variety of Sensors for User-Oriented IoT (SpineCouch)

Mentor: Asim Smailagic, Dan Siewiorek
Students:  Kyuin Lee, Raghu Mulukutla, Qian Yang (Poster, Video)

Original description
In the Internet of Things (IoT)  applications are based on large amounts of sensor data and making that data usable to everyday persons is becoming a challenge as well. The project uses context-aware and Internet of Things computing technology to aid care coordinators in keeping their patients healthy, happy, independent and safe in their respective homes. Taking care of elderly persons is becoming a real challenge in many countries. The system will (1) allow to view and add their patients’ information, (2) provide some data analytics, (3) gather medical and social/emotional data to provide a holistic view of the health status of patients, and (4) provide alerts and notifications when a patient deviates from their baseline health. We combine mobile and stationary sensors, as well as EMA (Ecological Momentary Assessment) surveys for parameters that cannot be sensed (e.g., social activity, mood).  We will aim to model people’s sleep patterns, physical activities, stress levels and social activities, showing end users details of their own behaviors and offering the community aggregated summaries. These technologies should enable self-monitoring and sharing of progress with healthcare providers.    
11. Efficient Large File Sharing on Cloudlets
Mentor: Jan Harkes
Students: Arushi Grover,  Preeti Murthy, Prathi Shastry
(Poster, Video)

Original description
The Coda Distributed File System (Coda) has multiple features that make it desirable in poorly connected environments. By its aggressive, persistent, whole-file caching strategy and ability to continue read/write operation even when the Coda servers are unreachable it makes it very well suited to provide file services for cloudlets that have possibly intermittend connectivity which are available close to a mobile end user.  However with several modern workloads such as MapReduce or video stream analysis that work with large, append only type files, the whole file caching strategy makes it inefficient to work with large files because we possibly send a multi-GB file back to the server multiple times.  Several things need to improve for Coda to tackle such large file more effectively. First of all, in the existing design Coda has no insight into individual read and write operations. By adding support for FUSE either directly or through a proxy process it would be possible to observe which parts of a file are updated or accessed.  Secondary there has to be some sort of immutable file storage on the server that allows clients to fetch data from an older version of a file (to be able to maintain the existing open-close consistency model. This could be implemented as an S3(-compatible) storage pool, or possibly a more efficient delta packing format, similar to git packfiles. 

From there on, there are many possible directions this work can take. On the write path it now becomes possible to track which parts of a file have actually been modified, and a binary delta can be generated and sent to the server instead of the whole file. On the read path it will be possible to fetch file contents on-demand as they are being accessed, with the caveat that we will probably lose performance and our ability to survive network failures because now only fragments of files may been cached.   Implementing this may require some kernel level work, deep knowledge of C/C++ programming and a lot of careful thinking about consistency in a distributed system.

Last updated 2016-12-16 by Satya