Dynamic localization control for mobile sensor networks
Sponsored Links
This presentation is the property of its rightful owner.
1 / 23

Dynamic Localization Control for Mobile Sensor Networks PowerPoint PPT Presentation


  • 53 Views
  • Uploaded on
  • Presentation posted in: General

Dynamic Localization Control for Mobile Sensor Networks. S. Tilak, V. Kolar, N. Abu-Ghazaleh, K. Kang (Computer Science Department, SUNY Binghamton). Agenda. Introduction to Localization Motivation Problem Definition Protocols Results Future work Conclusion.

Download Presentation

Dynamic Localization Control for Mobile Sensor Networks

An Image/Link below is provided (as is) to download presentation

Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author.While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.


- - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - -

Presentation Transcript


Dynamic Localization Control for Mobile Sensor Networks

S. Tilak, V. Kolar, N. Abu-Ghazaleh, K. Kang

(Computer Science Department, SUNY Binghamton)


Agenda

Introduction to Localization

Motivation

Problem Definition

Protocols

Results

Future work

Conclusion


Introduction to Localization

AVG Normal


Existing Research on Localization

  • Focus on Static Sensor Network

  • Existing Approaches:

    • -Range/Direction based

    • -calculate distance from beacons and triangulate

    • -Received Signal Strength (e.g., RADAR)

    • -Time of Arrival (e.g., GPS)

    • -Time Difference of Arrival (e.g., Cricket, Bat)

    • -Calculate angle from beacons and triangulate

    • -Proximity based

    • -Centroid (Bulusu 00)

    • -ATIP (Mobicom 2003)

    • -DV-hop

    • -MDS (MobiHoc 2003)

    • -single hop vs. multi-hop to beacon


Motivation

What about Mobile Sensor Networks ?

Interesting Energy-Accuracy trade off !


Problem Definition


Goals

Self-configuring

Light-weight

Enable Micro-monitoring

Application-specific

Scalable, distributed


Protocols

SFR (Static Fixed Rate)

DVM (Dynamic Velocity Monotonic)

MADRD (Mobility Aware Dead Reckoning Driven)


SFR

Localize every t seconds

Very simple to implement

Once Localize tag data with those coordinates till next localization

Energy expenditure independent of Mobility

Performance varies with Mobility

Existing Projects such as Zebranet use this approach (3 minutes).


DVM

Adaptive Protocol

Sensor Adapts its localization frequency to Mobility

Goal maintain error under application-specific tolerance

Compute current velocity and use it to decide next localization period

Once Localize tag data with those coordinates till next localization

Upper and Lower query threshold

Energy expenditure varies with Mobility

Performance almost invariant of Mobility


MADRD

Predictive Protocol

Estimate mobility pattern and use it to predict future localization

Localization triggered when actual mobility and predicted mobility differes by application-specific tolerance

Tag data with predicted coordinates (differs from SFR and DVM)

Changes in mobility model affect the performance

Upper and Lower query threshold

Energy expenditure varies with Mobility

Performance almost invariant of Mobility


MADRD State Diagram


Analysis of the Proposed Protocols

  • Constant Velocity model

    • SFR and DVM error increases linearly

    • MADRD estimates location precisely (no error)

  • Contant Velocity + pause

    • SFR and DVM error increasely linearly and stays there

  • MADRD has 0 initial error and then it increases linearly

  • Contant Vecloty + change in direction


  • Direction change


    Summary of Analysis

    • Error in non-predictive protocols increase with any mobility that moves the node away from its last localization point

    • Error in Predictive protocols increase only when the predictive model is inaccurate

      • Model estimation in incorrect

      • Model changes (pause, direction change)


    Instantenous Error Study


    Energy Expenditure Study

    DVM adapts

    4-5 m/s

    0.5-1 m/s


    Error versus Mobility and Pause Time

    SFR error increases linearly with mobility, DVM, MADRD not much change


    Accuracy versus Mobility and Pause Time


    Conclusion

    Explored interesting energy accuracy trade offs for mobile sensor network with three protocols

    Different velocities and pause time

    Adaptive and Predictive protocols can outperform static protocol

    If mobility model is predictable MADRD performs well

    MADRD performed well under all situations that we simulated

    Possible to design light-weight, self-configuring, and scalable protocols that reduce localization energy without sacrifying accuracy


    Future Work

    Implement all protocols on Motes

    Study protocols under more mobility models

    Event driven sensor network

    Incorporating application semantics such as data priorities


    Questions ?


    Thank You !!!


  • Login