Bubble-Sensing: A New Paradigm for Binding a Sensing Task to the Physical World using Mobile Phones Hong Lu, Nicholas D. Lane, Shane B. Eisenman, Andrew

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  • Bubble-Sensing: A New Paradigm for Binding a Sensing Taskto the Physical World using Mobile PhonesHong Lu , Nicholas D. Lane, Shane B. Eisenman, Andrew T. CampbellDartmouth College

  • IdeaGoalAttach sensing request to the area of interest

    ProblemsHold the bubble in the area of interestRecover from lost bubbleExploit heterogeneous devices

    IdeaOpportunistically leverage the devices that remain in the area or pass through to fulfill the sensing request and maintain the binding

  • Bubble SensingSensing Bubble(action, region, duration)

    IdeaOpportunistically leverage cell phone density within the area to maintain coverage

    Virtual rolesBubble creator Bubble anchorBubble carrierSensing nodeTake a photo here !!

  • Bubble Creation Phase

  • Bubble Maintenance PhaseBubble AnchorMobile SensorMobile SensorBubble Anchor

  • Bubble Restoration Phase

  • Bubble Restoration Phase

  • Test bed ImplementationDeviceNokia N95, Nokia N80 + BlueCel dongle

    Pys60

    WiFi based communication Ad-Hoc mode infrastructure mode4mw

    Beacon based WiFi localization

    The sensing task:Take sound clips

  • Sensing Coverage over Time

  • Fidelity

  • Related WorkMobile phone as a sensing deviceUCLA, UIUC, MIT, Ohio StateNokia, Motorola, Intel, MicrosoftExploit location information Routing, Geocastgeographic storage, DCS, GHTTheoretical workVirtual static node

  • Thanks for Listening

  • Sensing Coverage over Time

    ***Simulation of three representative techniques:Hop countingCentroidMCLZebranet based mobility model.Techniques sensitive to beacon or node density. Failure to produce location estimates (or estimates are highly inaccurate in the case of MCL)

    Although required, existing localization schemes are weak.One of the few examples of these sensor networks zebra net used for this simulation.Point was to examine the performance of three approaches to localizatoin and how they would fareIn this environment.They perform poorly.Quickly describe the simulation. Hey what are the axis. Tell tell them exactly what is shown. And which are the schemes and the setup.And note the MCL issue.

    Question someone may ask: - be ready for it - what is MCL? What is centroid? What is Amphrhous???MAKE SURE YOU RE-READ THESE PAPERS!!! Be nice to maybe talk about other applications of monte carlo??? In other areas?*Make sure you discuss how you built the classifier and what it does.

    What is the feature vector?

    Then discuss the performance of it.

    Make sure you read up about decision trees and the specifics of j48 before you forget the details.*Make sure you discuss how you built the classifier and what it does.

    What is the feature vector?

    Then discuss the performance of it.

    Make sure you read up about decision trees and the specifics of j48 before you forget the details.*Where to next?Analysis of ABL within a full scale testbed

    Stocastic analysis of systemRealistic outdoor experiments. Using metrosense testbed.

    **Make sure you discuss how you built the classifier and what it does.

    What is the feature vector?

    Then discuss the performance of it.

    Make sure you read up about decision trees and the specifics of j48 before you forget the details.