OTMCL: Orientation Trackingbased Localization for Mobile Sensor Networks

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OTMCL Orientation Tracking-based Localization
for Mobile Sensor Networks
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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

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The case of mobility in localization
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Our goal

• We are interested on positioning of low-powered,
  resource-constrained 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

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MCL Initialization
Nodes actual position
Nodes 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
Prediction New particles based on previous
estimated location and maximum velocity, vmax
Nodes actual position
Nodes last estimate
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Filtering
a
a
Indirect Anchor Within distance (r, 2r of that
anchors location.
Direct Anchor Within distance r of the anchors
location
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MCL Filtering
Nodes actual position
Binary filtering Samples which are not inside
the communication range of an anchor are discarded
r
Anchor
Invalid samples
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Re-sampling

• Repeat prediction and filtering until we obtain a
  minimum number of samples N.
• Final estimate is the average of all filtered
  samples
• 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 non-anchor
  nodes after they are localized
• Samples are weighted according to reliability of
  neighbors (non-binary filter)

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Sample Degradation

• Problem 1 Predicting samples with the wrong
  direction or velocity

• Problem 2 Previous location estimate is not
  well-localized

Why dont we tell where samples should be
generated?
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Proposal Orientation Tracking-based Monte Carlo
Localization (OTMCL)
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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 ß
• High-precision sensors ( ß 10o)
• Medium-precision sensors ( ß 30o, ß 45o)
• Low-precision sensors ( ß 90o)

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Analysis Convergence time
relative to communication range
stabilization phase
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
OTMCL is robust even when the anchor network is
sparse
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Analysis Speed variance
As speed increases, the larger is the sampling
area ? lower accuracy
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Analysis Communication Irregularity
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_at_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 gt 50
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Analysis Regular node density
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)

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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)