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Social Sonar

Social Sonar. Control Your World...Before Your Friends Do!. Presentation Outline. What Is Social Sonar ? Requirement Specifications Functional Requirements Non-Functional Requirements The Application Architecture The State Diagram. The Challenges of Our Design

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Social Sonar

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  1. Social Sonar • Control Your World...Before Your Friends Do!

  2. Presentation Outline • What Is Social Sonar? • Requirement Specifications • Functional Requirements • Non-Functional Requirements • The Application Architecture • The State Diagram • The Challenges of Our Design • The Application Architecture: • Main Object Identification • The UML Diagram • Presentation Review Marc

  3. The Problem Statement • At a glance: • Provide entertainment to users • Upon further inspection: • How to predict human movement • How will we do it? • Gather traces from users • Game will act as an incentive to provide the trace • Why is it important: • If you can predict human movement, many opportunities unfold • In ad-hoc routing...Implement efficient routingIn transportation...Reduce congestionSave fuelFor Advertising...Better ad targeting • Better product placement Michael

  4. Social Sonar: What Is It? • Social Gaming Application • Entertains users with interactive game play • Attack peers • Defend yourself • Collect in-game resources • Records location with sampling (discussed later on) • Gathers location traces for research Michael

  5. Social Sonar: Some Concept Art Michael

  6. Are There Any Existing Solutions? • Cell phone applications • NFC based proximity gaming for the Blackberry • Geo-proximity alarm • Tap Tap War • Google’s Ingress; a mobile reality game [Hanke] • RFID tags to recognize patient/ medicine information. • Some frameworks • JCAF; the Java Context Awareness Framework [Bardram] • Qualcomm's Gimbal Sathvik

  7. What Services Will We Provide • Location discovery service • People discovery service [An, Wang] • Infrastructure service for research • Context aware services [Bardram] • Geo-fencing Interest sensing An AWARE framework [Bardram] Sathvik

  8. Requirement Specifications:Functional Requirements • Must find enemies and friends: • Requires Google Maps API to plot users’ locations and set a vision radius • Must seamlessly detect other users around the active user • Must house a database of resources and use them for later purposes such as building fortresses or houses to defend a location • Must publish updates to the user in a non-invasive manner: • Toast messages for notifications Akshita

  9. Requirement Specifications:Non-Functional Requirements • Availability: • All times when in range of WiFi • Security concerns • Resolution, screen formfactors • Visual aesthetics • Support for multiple channels: WiFi, 3G. The application will prefer WiFi • Efficiency: • Ensure less battery consumption by minimizing GPS usage Akshita

  10. Use Case Diagrams:Menu Screen

  11. Use Case Diagrams:Game Screen Akshita

  12. Some Challenges and Our Solutions • No setupWeaknessesUser could root phone and change Android_IDUsing virtual box, a single person could make a an army • Privacy and Security:User LocationLocation collection Enable/DisableUser/Phone IdentityAndroid_ID (Settings.Secure.ANDROID_ID)Android 2.2 and laterProtects User Anonymity • Human Movement Prediction: • Location prediction algorithm and map matching [Lou] • UTDOA [Yi-Bing Lin, Chien-Chun Huang-Fu] • Sampling rate selection • Aggregation and distribution of location information: • Store location on central server database • Easily query for users and game elements in a local area • Weakness: • Devices cannot directly communicate with each other • Server must provide data to each device Yi Che & Michael

  13. The Algorithms:Location-Prediction Last Predicted Location Last Predicted Location Analyze Location Predictions Last Known Location • Our algorithm: • Is not used immediately • Not reliable for consistent use • Allows us to “fill” gaps and “cheat” • Influences from MANET-LPBR [Meghanathan] • The concept of coordinate windows Last Fine Update Time Last Prediction Last Fine Update Time Last Prediction Demonstration:Location-Prediction Algorithm Time Current Time Stability Threshold Stability Threshold Stability Threshold Sampling Threshold ... Marc

  14. Our Design Rationale • Sampling rate: • Fair balance between accuracy and power consumption • User can always switch to “intense mode” • Location prediction algorithm: • Allows us to “cheat” when obtaining location information • Limit the user’s field of vision: • Fits the theme of our game, and adds an element of balance Sathvik

  15. The State Diagram: The Diagram Application Architecture:The State Diagram The State Diagram:The Main States MAIN_MENU PLAYING PAUSED MAIN_MENU Presents the user with the main utility options of the game This is the first state of the game PLAYING The user is actively playing the game This is the second state of the game PAUSED The user was playing the game, however their play has been interrupted This is the third state of the game Yi Che

  16. Application Architecture:Main Object Identification MainMenu Monitors user input, instantiates correct Activity. Main entry point for the application. GameManager Ensures the proper maintenance of the game controller. SocialSonar Maintains the render thread, and game logic. RenderThread Redraws the screen and updates game asynchronously. LocationManager Provides consistent and accurate location information. Marc

  17. Application Architecture:The UML Diagram GameManager RenderThread LocationManager MainMenu SocialSonar Marc

  18. Presentation Review • What Is Social Sonar? • Requirement Specifications • Functional Requirements • Non-Functional Requirements • The Application Architecture • The State Diagram • The Challenges of Our Design • The Application Architecture: • The use-case diagram • Main Object Identification • The UML Diagram • Presentation Review Marc

  19. Bibliography • * Nilson, Martin, and Laura Feeney. Investigating the Energy Consumption of a Wireless Network Interface in an Ad Hoc Networking Environment. 2000.Jacob E. Bardram. The Java Context Awareness Framework (JCAF) – A Service Infrastructure and Programming Framework for Context-Aware Applications. 2005.Xueli An, Jing Wang. OPT – Online Person Tracking System for Context-awareness in Wireless Personal Network (Demo). 2006.Ralph Löwe, Peter Mandl, Michael Weber. Context Directory: A Context-Aware Service for Mobile Context-Aware Computing Applications by the Example of Google Android. 2012.Yi-Bing Lin, Chien-Chun Huang-Fu, Predicting Human Movement based on Telecom's Handoff in Mobile Networks. http://liny.csie.nctu.edu.tw/document/tmc11_PHM.pdfHanke, John . "Google Launches Ingress, a Worldwide Mobile Alternate Reality Game." allthingsd. (2012): n. page. Web. 28 Feb. 2013. <http://allthingsd.com/20121115/google-launches-ingress-a-worldwide-mobile-alternate-reality-game/>. • Meghanathan , N. "Location Prediction Based Routing Protocol for Mobile Ad Hoc Networks.". Jackson: Dept. of Comput. Sci., Jackson State Univ., 2008. Print. <http://ieeexplore.ieee.org.lp.hscl.ufl.edu/stamp/stamp.jsp?tp=&arnumber=4697891>. • Kumar, Udayan. iTrust.  Ahmed Helmy. 2012. Application. 7 Mar 2013. <http://128.227.176.22:8182/iTrust.html>. • Bill Schilit, Norman Adams, Roy Want. “Context Aware Computing Applications” • http://graphics.cs.columbia.edu/courses/mobwear/resources/schilit-mcsa94.pdf • Perez, Sarah. "Intelligent, Context-Aware Personal Assistant App “Friday” Makes Its Public Debut."TechCrunch. 12 Jul 2012: n. page. Web. 7 Mar. 2013. • * Stemm, Mark, Paul Gauthier, Daishi Harada, and Randy Katz. "Reducing Power Consumption of Network Interfaces in Hand-Held Devices." CiteSeerX. Berkeley, n.d. Web. 18 Feb 2013. <http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.39.8384>. • Agarwal, Yuvraj, Steven Hodges, Ranveer Chandra, et al.Augmenting Network Interfaces to Reduce PC Energy Usage. San Diego: 2009. • Sohan, Ripduman, Andrew Moore, et al, and Andrew Rice.Characterizing 10 Gbps Network Interface Energy Consumption. Cambridge: 2010. • Lou, Yin, Chengyang Zhang, Yu Zheng, et al. "Map-Matching for Low-Sampling-Rate GPS Trajectories.". Houston: Microsoft Research, 2010. Web. 4 Apr. 2013. <http://research.microsoft.com/pubs/105051/Map-Matching for Low-Sampling-Rate GPS Trajectories-cameraReady.pdf>.

  20. Are there any questions?

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