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

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

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

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

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

Analysis – Anchor density

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OTMCL is robust even when the anchor network is sparse

Analysis – Speed variance

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As speed increases, the larger is the sampling area lower accuracy

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

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

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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

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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