Adaptive Wireless Biomedical Capsule Localization and Tracking
OTMCL: Orientation Tracking-based Localization for Mobile Sensor Networks
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Transcript of OTMCL: Orientation Tracking-based Localization for Mobile Sensor Networks
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INSS 2009June, 18th 2009Pittsburgh, USA
Marcelo Martins, Hongyang Chen and Kaoru SezakiUniversity of Tokyo, Japan
OTMCL: Orientation Tracking-based Localization for Mobile Sensor Networks
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Location awareness
Localization is an important component of WSNsInterpreting data from sensors requires context
Location and sampling time?
ProtocolsSecurity systems (e.g., wormhole attacks)Network coverageGeocastingLocation-based routing
Sensor Net applicationsEnvironment monitoringEvent trackingMapping
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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 algorithmsNodes cooperate with each otherAnchor-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 in positioning low-powered, resource-constrained sensor nodes
A (reasonably) accurate positioning system for mobile networks
Low-density, arbitrarily placed anchors and regular nodesRange-free: no special ranging hardwareLow communication and computational overheadAdapted 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 mobilityProbabilistic approach: explicitly considers the impreciseness of location estimates
Maximum Likelihood Estimator (MLE)Maximum A Posteriori (MAP)Least SquaresKalman FilterParticle Filtering (Sequential Monte Carlo or SMC)
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Sequential Monte Carlo Localization
Monte Carlo Localization (MCL) [Hu04]Locations are probability distributionsSequentially updated using Monte Carlo sampling as nodes move and anchors are discovered
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MCL: Initialization
Initialization: Node has no knowledge of its location.
L0 = { set of N random locations in the deployment area }
Node’s actual position
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Node’s estimate
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MCL: Prediction
Node’s actual position
Prediction: New particles based on previous estimated location and maximum velocity, vmax
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Node’s last estimate
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Filtering
Indirect Anchor
Within distance (r, 2r] of anchor
Direct Anchor
Node is within distance r ofanchor
a a
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MCL : Filtering
Node’s actual positionNode’s actual position
r
Anchor
Invalid samplesInvalid samples
Binary filtering: Samples which are not inside the communication range of anchors are discarded
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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 non-anchor nodes after they are localizedSamples 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)
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Sensor bias
Inertial sensor is subject to bias due toMagnetic interferenceTemperature variationErroneous calibration
Affects velocity and orientation estimation during movement Lower localization accuracy
No assumptions about hardwareAnalyses 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
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OTMCL achieves a decent performance even when the inertial sensor is under heavy bias
relative to communication range
~ 7m
stabilization phase
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Analysis – Communication overhead
Reducing power consumption is a primary issue in WSNsLimited batteries
Inhospitable scenarios
Assumes no data aggregation, compressionOTMCL needs less information to achieve similar accuracy to MSL
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Analysis – Anchor density
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OTMCL is robust even when the anchor network is sparse
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Analysis – Speed variance
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As speed increases, the larger is the sampling area lower accuracy
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Analysis – Communication Irregularity
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OTMCL is robust to radio irregularity. Dead reckoning is responsible for maintaining
accuracy
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Conclusion
Monte Carlo localizationAchieves 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 workAdaptive localization (e.g., variable sampling rate, variable sample number)Feasibility in a real WSN
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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
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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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