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.
MobiArch’11
I just attended and presented a paper about ErdOS architecture in MobiArch'11, a workshop that this year was colocated with MobiSys. There were 7 presentations and they covered topics such as Multipath TCP, energy efficiency at different layers on a mobile handset and MANETs.
The first session was completely focused on multipath TCP. Cristopher Pluntke from UCL showed how Multipath TCP can reduce the energy consumption of mobile devices by trying to combine the best of cellular networks and WiFi worlds. In particular, their complementary power modes. The main component of their system is an scheduler (a Markov Decision Process) that takes into exploits a fairly accurate power model of both WiFi and Cellular interfaces to efficiently switch between these two interfaces to minimise the energy cost. As the author highlighted, this energy model is hardware dependent. Nevertheless, my feeling is that this system tries to make an optimal and efficient usage of all the available wireless interfaces without necessarily taking advantage of them simultaneously. In his own words, both the scheduler and the model can be extended, so they can consider other aspects such as network latency and the SNR of the link in order to take better decisions. The second presentation in this session was done by Costin Raiciu (University Politehnica Bucharest). In his paper Opportunistic Mobility with Multipath TCP, he suggests that the best layer to handle mobility is at the transport layer and Multipath TCP can play an important role to solve some of the issues related with mobility. One of the arguments of the papers is that, in addition to achieve a better throughput and support both IPv4 and IPv6 links, it can also provide energy savings. They carefully evaluated the overhead of supporting MPTCP on the mobile handset in terms of CPU, network and memory.
The second session was mainly about MANETs and how they can be integrated into a future mobile Internet architecture. The first talk was by David Bild (University of Michigan) about Using predictable mobility patterns to support scalable and secure MANETs of Hanheld devices. The paper looks more like a positioning paper and, in his opinion, location-centric networking can provide secure communications, specially in the case of personal communications between friends and family. His argument is supported by Barabasi's study about the predictability of human mobility so location can solve some of the open security issues in MANETs. The second paper was entitled GSTAR: Generalized Storage-Aware Routing for MobilityFirst in the Future Mobile Internet, presented by Samuel Nelson (Rutgers University). This paper is part of MobilityFirst Project and it clearly reminded me of a combination of Haggle-project with Content-centric networks. The authors consider that the fixed-host/server model that has dominated the Internet since its conception needs to evolve and it must consider mobility as a core component. Consequently, they suggest that giving support to DTNs/ad-hoc nets and Content-Centric networks will be necessary. Some of the problems they aim to address are host and network mobility (how can entities stay reachable), varying levels of wireless link quality (higher-level protocols respond), varying levels of connectivity (can disconnection be handled within the network itself?) and multi-homing. They propose that the naming system (also for content) must be human readable and context-based. They also aim to include intelligence in the network by providing it with resources and by increasing the possible routing options such as seamless routing protocols for local scale routing.
The last paper in this session was not completely in the scope of MANETs. Hossein Falaki (UCLA) presented SystemSens, an interesting tool for monitoring usage in smartphone research deployments. Despite its similarities with Device Analyzer (the work by Daniel Wagner and Andrew Rice, DTG group), SystemSens is considered mainly as a debugging tool. This application (runs as a background service and does not require rooting the handsets) can help developers to better understand the impact of their applications on a real systems on the wild before making a final deployment. It monitors variables that range from battery usage to CPU load and network state. The traces are initially logged locally on a SQL DB and then uploaded to a server. Developers can consequently identify unexpected behavior from the users and application and they can also identify bugs.
The final session was about energy efficiency. Hao Liu from Tsinghua University presented TailTheft, a paper related to TailEnder and TOP. This approach is not exclusively based on reducing the number of tails (i.e. the FACH power state on cellular networks) and on reducing the duration of the tail as the previous ones. In this case, the authors propose creating virtual tails to allow the system prefetching and deferring transfers via a dual queue scheduling algorithm. Applications can, in fact, predict its future transmission with a reasonable accuracy so this solution is conceived as an application-layer optimisation. Â
Finally, the last presentation of the workshop was our ErdOS project. In our case, we propose a different approach to save energy at the operating system level by extending the duration of time that resources can remain in low power modes with two techniques:
a) by predicting when they are going to be accessed by applications and by predicting the state of the computing resources based on contextual information.
b) by enabling opportunistic access to resources available in co-located devices using low power interfaces such as bluetooth.
ICDCS 2011
just before the workshop in france, i attended icdcs in the states. few papers follow:
Efficient and Private Access to Outsourced Data (pdf). Say that you outsource your private data to "the cloud". The authors of this paper proposed a new data strucutre with which you can efficiently access your outsourced data while guaranteeing content, access, and pattern confidentiality from any observer, including the cloud provider.
Dissecting Video Server Selection Strategies in the YouTube CDN (pdf). Ruben presented an extensive study of the YouTube CDN. The goal of this study was to identify the factors that impact how video requests are served by data centers. They found that "the YouTube infrastructure has been completely redesigned compared to the one previously analyzed in the literature. In the new design, most YouTube requests are directed to a preferred data center and the RTT between users and data centers plays a role in the video server selection process. More surprisingly, however, our analysis also indicates a significant number of instances (at least 10% in all our datasets) where videos are served from non-preferred data centers."
An Energy-efficient Markov Chain-based Randomized Duty Cycling Scheme for Wireless Sensor Networks. Giacomo presented a new duty cycling scheme for sensor nodes that is energy-efficient and is based on Markov chains. In the past, Giacomo has done some interesting work in the area of "Internet of Things" at Sun Labs: he built a Web-based application that analyses and visualise large, heterogeneous, and live data streams from a variety of devices (pdf).
Efficient Online WiFi Delivery of Layered-Coding Media using Inter-layer Network Coding (pdf). Dimitrios studied the problem of how to deal with the problem of client diversity when video is multicasted to multiple clients over a wireless LAN. He showed that the traditional triangular scheme for inter-layer network coding performs poorly. He thus proposed a new online video delivery scheme that can be deployed behind the wireless AP.
Temporal Complex Network Measures for Mobile Malware Containment
Picture the scene: you've bought a shiny new smartphone and have been customising it all weekend by installing various apps from the app store, however the following week you encounter a run of bad luck...
...first your house is burgled when you're at work, next your credit card is maxed out, your friends have been receiving spam text messages from you and to top it off, weeks later, some of your colleagues have had the same experience; what is going on?
Little beknown to you, within one of these seemingly innocuous apps lurks a piece of mobile malware (mobware) which has access to a wealth of personal information which an attacker can access remotely. Â