Dynamic localization control for mobile sensor networks
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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.

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Dynamic localization control for mobile sensor networks
Dynamic Localization Control for Mobile Sensor Networks

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

(Computer Science Department, SUNY Binghamton)


Agenda
Agenda

Introduction to Localization

Motivation

Problem Definition

Protocols

Results

Future work

Conclusion



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
Problem Definition


Goals
Goals

Self-configuring

Light-weight

Enable Micro-monitoring

Application-specific

Scalable, distributed


Protocols
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
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



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



  • Summary of analysis
    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)



    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



    Conclusion
    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
    Future Work

    Implement all protocols on Motes

    Study protocols under more mobility models

    Event driven sensor network

    Incorporating application semantics such as data priorities




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