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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

slide4

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
slide5

Motivation

What about Mobile Sensor Networks ?

Interesting Energy-Accuracy trade off !

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)

slide9
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).

slide10
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

slide13

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)
slide17

Energy Expenditure Study

DVM adapts

4-5 m/s

0.5-1 m/s

slide18

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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