Mobisys’11. Day 1 $

MobiSys started this morning with 3 sessions about mobile applications and services, energy-efficient management of displays and crowd-sourcing apps. Researchers affiliated to 26 different institutions were within the co-authors of the papers. The most successful ones are Duke University (4 papers), At&T (4 papers), Univ. Michigan (3 papers) and Univ. Southern California (3 papers). The keynote was given by Edward W. Felten from the Federal Trade Commission about how the FTC works.

Session 1. Services and Use Cases

The first presentation was a quite cool idea from Duke University called: TagSense: A Smartphone-based Approach to Automatic Image Tagging. Their system proposed a system for automatically tagging pictures by exploiting all the sensors and contextual information available on modern smartphones: WiFi ad-hoc network, Compass, Light sensors (to identify whether the handset is indoors or outdoors), Microphone, Accelerometer (movement of the user), Gyroscope and GPS (location). When the camera application is launched, it creates a WiFi ad-hoc network with all the nearby devices and they exchange contextual information to add rich metadata to the picture captured. One of the challenges they tackled was about discerning if the user was moving, posing, facing the camera, etc. They implemented a prototype on Android and they evaluated it with more than 200 pics. The paper compares the accuracy of automatic tagging results with the metadata that was manually added in Picassa and iPhoto. With this system, the number of tags missed is reduced considerably. Nevertheless, the system left some open research challenges such as user authentication and a system performance evaluation.

A second paper by also by Duke University researchers was Using Mobile Phones to Write in Air (it was an extension of a HotMobile paper in 2009). In this case, the idea is about using accelerometers to allow writing in the air using the phone as a pen. The accelerometer records the movement and they display the text on the screen after being processed on a server running Matlab. Some of the research challenges that they had to face were about filtering high frequency components from human hand vibrations (removed with a low-pass filter), recognizing the symbols (pre-loaded pattern recognition, it reminds me of how MS Kinect works), identifying pen lifting gestures and also dealing with hand rotation while writing (accelerometers only measure linear acceleration, wii uses a gyroscope to solve this issue). The system seems to work nicely and they said that it has been tested in patients unable to write manually.

The following presentation was Finding MiMo: Tracing a Missing Mobile Phone using Daily Observations from Yonsei University. This system allows finding lost/stolen mobile handsets in indoors environments. The authors claim that it solves some of the limitations of services such as Apple Mobile Me, which can be constrained by the availability of network coverage and battery capacity limitations. They support an adaptive algorithm for sensing and they also leverage several indoors location techniques.

Odessa: Enabling Interactive Perception Applications on Mobile Devices by M. Ra (Univ. of Southern California), A. Sheth (Intel Labs), L. Mummert (Intel Labs), P. Pillai (Intel Labs), D. Wetherall (Univ. of Washington) and R. Govindan (Univ. of Southern California), is about off-loading computation to the cloud to solve face, objects, pose and gesture recognition problems. Their system adapts at runtime and decides when and how to offload computation efficiently to the server based on the availability of resources (mainly network). They found that off-loading and parallelism choices should be dynamic, even for a given application, as performance depends on scene complexity as well as environmental factors such as the network and device capabilities. This piece of work is related with previous projects such as Spectra, NWSLite and Maui.

Session 2. Games and Displays

The first paper, entitled Adaptive Display Power Management for Mobile Games was a piece of work by Balan's group at the Singapore Management University. This problem tries to minimise the impact of interactive apps such as games that require having a power-hungry resource like the display active for long periods of time while trying to do not impact on the user experience. As an example, the show how while playing a youtube video, 45-50% of the energy consumption is taken by the display, cellular network takes 35-40% and the CPU 4-15%. This system dynamically combines screen brightness to reduce the energy consumption with non-linear gamma correction techniques per frame to compensate the negative effect of the brightness reduction. They also conducted a user study with 5 students to understand human thresholds for brightness compensation.

Switchboard: A Matchmaking System for Multiplayer Mobile Games by J. Manweiler (Duke Univ.), S. Agarwal (Microsoft Research), M. Zhang (Microsoft Research), R. Choudhury (Duke Univ.), P. Bahl (Microsoft Research), tries to predict the network conditions of mobile users to provide a good mobile gaming experience to the users. They presented a centralised service that monitors the latency between the game players to matchmaking them in mobile games. They tackled some scalability issues such as grouping users in viable game sessions based on their network properties.

Chameleon: A Color-Adaptive Web Browser for Mobile OLED Displays by M. Dong (Rice Univ.) and L. Zhong (Rice Univ.), take advantage of the well known observation about the impact of colors displayed on OLED screens. The energy consumption can vary from 0.5 W (almost black screen) to 2W (white screen). The power consumption of a OLED display increases linearly with the number of pixels, whle the energy consumption per pixel depends on the different leds that are active. In fact, 65% of the pixels on most of the common websites are white and this unnecessarily imposes a higher energy consumption on mobile handsets. Generally, green and red pixels are more energy-efficient than blue ones in most of the handsets so they propose transforming the colour of GUI objects on the display to make it more energy efficient in a similar fashion to Google Black. The 3 phases of their transformation are "color counting" (finding histogram of the GUI components), "color mapping" and "color painting". They also allow the user to use different color transformations for different websites.

Session 3. Crowdsourcing

In this session, some interesting applications were proposed such as Real-Time Trip Information Service for a Large Taxi Fleet by Balan (Singapore Mgmt Univ.). This application collects information about taxis availability and it finds routes/similar trips for the customers based on starting point, ending point, distance and time. They described how they had to find and eliminate sources of errors (e.g. weather) and how they used dynamic clustering (KD-Trees) to solve the problem. The second application was AppJoy: Personalized Mobile Application Discovery by B. Yan (Univ. of Massachusetts, Lowell) and G. Chen (Univ. of Massachusetts, Lowell). This is basically a recommendation engine for mobile apps according to user download history, ratings and passive information about how often users run those applications. They claim that the users that installed apps via AppJoy interacted with those apps more. They want to extend it to a context-aware recommendation engine. Finally, SignalGuru: Leveraging Mobile Phones for Collaborative Traffic Signal Schedule Advisory by E. Koukoumidis (Princeton Univ.), L. Peh (MIT) and M. Martonosi (Princeton Univ.), is a traffic signaling advisory system. It identifies traffic lights using the camera and tries to predict when they will turn red/green. They claim that this can considerably save an important amount of fuel to the drivers (20%) so it reduces the carbon footprint. The predictions are achieved by leveraging crowd-sourcing so cars collaborate and share information to identify those transitions. This system also uses sensors such as accelerometer and gyro-based image detection.