MetroTrack - MetroTrack Presented By Philip Shibly...

Info icon This preview shows pages 1–7. Sign up to view the full content.

View Full Document Right Arrow Icon
MetroTrack Presented By Philip Shibly Predictive Tracking of Mobile Events Using Mobile Phones Gahng-Seop Ahn, Mirco Musolesi, Hong Lu, Reza Olfati-Saber, and Andrew T. Campbell, “Metro Track: Predictive Tracking of Mobile Events Using Mobile Phones.”The City University of New York, USA, [email protected] University of St. Andrews, United Kingdom Dartmouth College, Hanover, NH, USA
Image of page 1

Info iconThis preview has intentionally blurred sections. Sign up to view the full version.

View Full Document Right Arrow Icon
Contents What is MetroTrack? Initial Pros/Cons Framework Information-Driven Tracking Prediction-Based Recovery Assumptions Prediction Algorithm Distributed Kalman-Consensus Filter Experiments and Simulations
Image of page 2
What is MetroTrack Mobile Phone Event Tracking System Tracks moving targets by collaborative sensing devices. Predicts future location of a target that may be lost during tracking. Does not rely on static networks or backend computation, but rather mobile users, so it is susceptible to spacial density, user participation, and realtime computation/feedback needs.
Image of page 3

Info iconThis preview has intentionally blurred sections. Sign up to view the full version.

View Full Document Right Arrow Icon
Initial Pros/Cons Relies on participatory users. Does not rely on user action. Needs dense network of users. Why would someone participate to track someone else's target? Why participate at all? Mobility of users is unpredictable and uncontrollable. Requires common sensors between users. Requires application to be running. Battery consumption? Does not rely on central nodes. Does not use node grouping. No back-end requirements.
Image of page 4
Framework MetroTrack consists of two algorithms (1) Information-Driven Tracking The sensor node begins tracking when certain criteria is met. Forwards tracking task to neighboring nodes.
Image of page 5

Info iconThis preview has intentionally blurred sections. Sign up to view the full version.

View Full Document Right Arrow Icon
Framework (cont.) (2) Prediction-Based Recovery If a nodes neighbor(s) do not report a tracked event task, then it is assumed that the target is lost.
Image of page 6
Image of page 7
This is the end of the preview. Sign up to access the rest of the document.

{[ snackBarMessage ]}

What students are saying

  • Left Quote Icon

    As a current student on this bumpy collegiate pathway, I stumbled upon Course Hero, where I can find study resources for nearly all my courses, get online help from tutors 24/7, and even share my old projects, papers, and lecture notes with other students.

    Student Picture

    Kiran Temple University Fox School of Business ‘17, Course Hero Intern

  • Left Quote Icon

    I cannot even describe how much Course Hero helped me this summer. It’s truly become something I can always rely on and help me. In the end, I was not only able to survive summer classes, but I was able to thrive thanks to Course Hero.

    Student Picture

    Dana University of Pennsylvania ‘17, Course Hero Intern

  • Left Quote Icon

    The ability to access any university’s resources through Course Hero proved invaluable in my case. I was behind on Tulane coursework and actually used UCLA’s materials to help me move forward and get everything together on time.

    Student Picture

    Jill Tulane University ‘16, Course Hero Intern