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 !!!