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The Smart Party A Personalized Location-Aware Multimedia Experience Kevin EusticeNam Nguyen V. Ramakrishna Dr. Leonard Kleinrock Dr. Peter Reiher Location-Driven.

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Presentation on theme: "The Smart Party A Personalized Location-Aware Multimedia Experience Kevin EusticeNam Nguyen V. Ramakrishna Dr. Leonard Kleinrock Dr. Peter Reiher Location-Driven."— Presentation transcript:

1 The Smart Party A Personalized Location-Aware Multimedia Experience Kevin EusticeNam Nguyen V. Ramakrishna Dr. Leonard Kleinrock Dr. Peter Reiher Location-Driven Interactive FictionIntelligent Museum Gallery Access annotation, shared content from tour group Localize within gallery and determine objects of interest The Panoply Middleware Functions Secure sphere management: discovery, formation and reconfiguration Scoping event interest and dissemination Providing primitives for sphere collaboration Dynamic management of policies and system state Inter-sphere interaction and dynamic access control through negotiation Management of a database of vouchers: electronic credentials that certify arbitrary properties of a sphere Provide primitives for the design of social- and location-sensitive applications like the Smart Party Other Supported Location-Sensitive Social Applications Our Vision Dynamic Playlists and Music Transfers Inference of Location Context Device Configuration & Collaboration Socially Aware Technology Spheres of Influence Automatically discovers and organizes devices into groups, or spheres, based on: social groups, physical location or proximity, task, network Manages: network connectivity, service discovery, location and social context Location Sphere Personal Sphere Social Sphere Produce better user experience by making mobile consumer devices cooperate when in proximity Bring people together; don’t keep them apart Leverage preferences and desires of individual users Use groups and Panoply for organization & management Mobile ubiquitous environment is complex and unpredictable, requiring better tools : That allow simplifying generalizations, like spheres of influence For debugging and evaluation of complex cooperation and competition scenarios Party guests carry mobile devices with songs and preferences Goal: Maximize user satisfaction through collaboration Criteria for music selection: song, artist, genre, album, period Protocol Room requests song suggestions from users Room makes selection; requests song delivery User device possessing that song delivers it to the playback device Songs are cached for efficient bandwidth use Iterative Context Refinement Mapping observed network to party location Trigger appropriate application that runs on guests’ mobile devices Digital Credentials Host provides vouchers to guests as invitations prior to the party Dynamic Collaboration Negotiation between guest device and server allows user into the party Deliver map of to guest devices Switch rooms based on the map as guest moves around Collaborates with room servers and other devices for musical experience Social Sphere (Team B) Location Sphere (Westwood) Social Sphere (Team A) Customized Location and Group-Aware Experience for Individual Visitors and Tour Groups Team-driven Non-Linear Narrative Dynamically Affected by User Actions at Designated Locations Each Room Maintains a dynamic playlist Plays music that represents the collective preference of its current occupants Semantic Maps Associate arbitrarily sized semantic regions with observed wireless networks Semantic regions: Family, Living and Dining Rooms Localization Scheme A combination of scene analysis and attenuation monitoring Device activates appropriate voucher based on the detected environment Voucher indicates which network to join Observed signal strengths from nearby 802.11 wireless access points are characteristic to a semantic region Advantages of Localization Technology Requires no deployment of custom hardware Can distinguish semantic regions on the basis of altitude, unlike GPS Need to build map through observations prior to inference Rich User Experience Intersection of social group and location contexts Dynamic Interactions Showcases different models: cooperation, competition, even active opposition Reconciling Guest Preferences and Selecting Songs Basic round-robin selection Dynamic user voting Leader-based Democratic model Important Metrics of Success Fairness: give everyone an equal chance to suggest a song of his choice Satisfaction: provide better average user experience over a period of time Occasional tradeoff required between these metrics I’ve joined the Party! Looks like there’s a Party going on! I can see the Party environment! Found a Party voucher! VOUCHER Delivering Map of Party Locations LIVING ROOM DINING ROOM FAMILY ROOM OUTSIDE THE PARTY ENVIRONMENT INSIDE THE PARTY ENVIRONMENT ROCK JAZZ I can’t stand the music here! The dining room is better suited to my tastes. ROCK JAZZ HIP-HOP SONG PLAYLIST HIP-HOP ROCK SONG PLAYLIST A ubicomp application to dynamically customize the music played at a party to the tastes of the party-goers This research was supported by the National Science Foundation (NSF): Grant # CNS 0427748


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