INSS 2009
June, 18th 2009
Pittsburgh, USA
OTMCL: Orientation Tracking-based
Localization for Mobile Sensor Networks
Marcelo Martins, Hongyang Chen and Kaoru Sezaki
University of Tokyo, Japan
Location awareness
Localization is an important component of WSNs
Interpreting data from sensors requires context
Location and sampling time?
Protocols
Security systems (e.g., wormhole attacks)
Network coverage
Geocasting
Location-based routing
Sensor Net applications
Environment monitoring
Event tracking
Mapping
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How can we determine location?
GNSS receiver (e.g., GPS, GLONASS)
 Consider cost, form factor, inaccessibility, lack of line of sight
Cooperative localization algorithms
Nodes cooperate with each other
Anchor-based case:
Reference points (anchors) help other nodes estimate their
positions
3
The case of mobility in localization
Static networks
Mobile networks
• One-time or lowfrequency
activity
• Refinement
process
• Can be
terminated
• Must be invoked
periodically
• Update process
• Must exist as
long as node
moves
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Our goal
We are interested in positioning low-powered, resourceconstrained sensor nodes
A (reasonably) accurate positioning system for mobile
networks
Low-density, arbitrarily placed anchors and regular
nodes
Range-free: no special ranging hardware
Low communication and computational overhead
Adapted to the MANET model
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Probabilistic methods
Classic localization algorithms (DV-Hop, Centroid, APIT,
etc.) compute the location directly and do not target mobility
Probabilistic approach: explicitly considers the
impreciseness of location estimates
Maximum Likelihood Estimator (MLE)
Maximum A Posteriori (MAP)
Least Squares
Kalman Filter
Particle Filtering (Sequential Monte Carlo or SMC)
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Sequential Monte Carlo Localization
Monte Carlo Localization (MCL) [Hu04]
Locations are probability distributions
Sequentially updated using Monte Carlo sampling as
nodes move and anchors are discovered
(Movement)
Filtering
Prediction
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MCL: Initialization
Node’s actual position
Node’s estimate
Initialization: Node has no knowledge of its location.
L0 = { set of N random locations in the deployment area }
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MCL: Prediction
Node’s actual position
Node’s last estimate
Prediction: New particles
based on previous
estimated location and
maximum velocity, vmax
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Filtering
a
a
Direct Anchor
Indirect Anchor
Node is within distance r of
anchor
Within distance (r, 2r] of
anchor
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MCL : Filtering
Node’s actual position
Binary filtering: Samples
which are not inside the
communication range of
anchors are discarded
r
Anchor
Invalid
samples
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Re-sampling
1. Repeat prediction and
filtering until we obtain
a minimum number of
samples N.
2. Final estimate is the
average of all filtered
samples
3. If no samples found,
reposition at the center
of deployment area
(initialization)
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Other SMC-based works
MCB [Baggio08]
Better prediction: smaller
sampling area using
neighbor coordinates
MSL [Rudhafshani07]
Better filtering: use
information from nonanchor nodes after they are
localized
Samples are weighted
according to reliability of
neighbors (non-binary filter)
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Issue: Sample degradation
Problem 1: Predicted samples with
wrong direction or velocity
Problem 2: Previous location
estimate is not well-localized
Why don’t we tell where samples should be
generated?
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Proposal: Orientation Tracking-based Monte
Carlo Localization (OTMCL)
Orientation Tracking
• Sensor information to predict
direction of movement
• Discover direction using a set
of sensors (e.g. gyroscope,
accelerometer, magnetometer)
• Prediction: Generated samples
move inside disc area
determined by α (measured
angle) and β (variance)
• Re-sampling: If no samples are
found, perform dead
reckoning
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Sensor bias
Inertial sensor is subject to bias due to
Magnetic interference
Temperature variation
Erroneous calibration
Affects velocity and orientation estimation during movement
Lower localization accuracy
No assumptions about hardware
Analyses use 3 categories of nodes for OTMCL based on β
High-precision sensors ( β = 10o)
Medium-precision sensors ( β = 30o, β = 45o)
Low-precision sensors ( β = 90o)
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Analysis – Convergence time
Simulation
Parameters
relative to communication range
stabilization phase
•
•
•
•
•
•
•
•
•
•
Area: 500 x 500 m2
Number of nodes: 320
Number of anchors: 32
Sample set: 50
Anchor density: 1
Node density: 10
Radio range: 50 m
Max velocity: 10 m/s
Mobility model: RWP*
Full mobile scenario
~ 7m
OTMCL achieves a decent performance even
when the inertial sensor is under heavy bias
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Analysis – Communication overhead
Reducing power consumption is a primary issue in WSNs
Limited batteries
Inhospitable scenarios
Assumes no data aggregation, compression
OTMCL needs less information to achieve similar accuracy to
MSL
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Analysis – Anchor density
Simulation
Parameters
•
•
•
•
•
•
•
•
•
Area: 500 x 500 m2
Number of nodes: 320
Number of anchors: 32
Sample set: 50
Node density: 10
Radio range: 50 m
Max velocity: 10 m/s
Mobility model: RWP*
Full mobile scenario
OTMCL is robust even when the anchor
network is sparse
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Analysis – Speed variance
Simulation
Parameters
•
•
•
•
•
•
•
•
•
Area: 500 x 500 m2
Number of nodes: 320
Number of anchors: 32
Sample set: 50
Anchor density: 1
Node density: 10
Radio range: 50 m
Mobility model: RWP*
Full mobile scenario
As speed increases, the larger is the
sampling area  lower accuracy
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Analysis – Communication Irregularity
Degree of Irregularity
[He03]
OTMCL is robust to radio irregularity. Dead
reckoning is responsible for maintaining
accuracy
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Conclusion
Monte Carlo localization
Achieves accurate localization cheaply with low anchor density
Orientation data promotes higher accuracy even on adverse
conditions (low density, communication errors)
Our contribution:
A positioning system with limited communication requirements,
improved accuracy and robustness to communication failures
Future work
Adaptive localization (e.g., variable sampling rate, variable
sample number)
Feasibility in a real WSN
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Thank you for your attention
martins@mcl.iis.u-tokyo.ac.jp
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APPENDIX
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OTMCL: Necessary number of samples
Estimate error fairly stable when N > 50
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Analysis – Regular node density
Simulation
Parameters
•
•
•
•
•
•
•
•
•
Area: 500 x 500 m2
Number of nodes: 320
Number of anchors: 32
Sample set: 50
Anchor density: 1
Radio range: 50 m
Max velocity: 10 m/s
Mobility model: RWP*
Full mobile scenario
OTMCL is robust even when the anchor
network is sparse
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Is it feasible? (On computational overhead)
Impact of sampling (trials until fill sample set)
Algorithm
Avg. # of sampling trials
(DOI = 0.0)
MCL
1933.1077
MCB
559.796
MSL
2401.2508
ZJL
597.8802
OTMCL (β = 10º)
391.6977
OTMCL (β = 45º)
746.1909
OTMCL (β = 90º)
1109.4819
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Radio model
 Upper & lower bounds on signal
strength
 Beyond UB, all nodes are out of
communication range
 Within LB, every node is within the
comm. range
 Between LB & UB, there is (1)
symmetric communication, (2)
unidirectional comm., or (3) no
comm.
 Degree of Irregularity (DOI)
([Zhou04])
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