dynamic localization control for mobile sensor networks s. tilak, v. kolar, n. abu-ghazaleh, k. kang...

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 NetworkExisting 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

4-5 m/s 0.5-1 m/sDVM adapts

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 !!!