Otmcl orientation tracking based localization for mobile sensor networks
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OTMCL: Orientation Tracking-based Localization for Mobile Sensor Networks. 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

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OTMCL: Orientation Tracking-based Localization for Mobile Sensor Networks

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Otmcl orientation tracking based localization for mobile sensor networks

OTMCL: Orientation Tracking-based Localization for Mobile Sensor Networks


Location awareness

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


How can we determine location

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


The case of mobility in localization

The case of mobility in localization


Our goal

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 nodes

    • Range-free: no special ranging hardware

    • Low communication and computational overhead

    • Adapted to the MANET model


Probabilistic methods

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)‏


Sequential monte carlo localization

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


Mcl initialization

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 }


Mcl prediction

MCL: Prediction

Prediction: New particles based on previous estimated location and maximum velocity, vmax

Node’s actual position

Node’s last estimate


Filtering

Filtering

a

a

Indirect Anchor

Within distance (r, 2r] of anchor

Direct Anchor

Node is within distance r of

anchor


Mcl filtering

MCL : Filtering

Node’s actual position

Binary filtering: Samples which are not inside the communication range of anchors are discarded

r

Anchor

Invalid

samples


Re sampling

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)


Other smc based works

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)


Issue sample degradation

Problem 1: Predicted samples with wrong direction or velocity

Problem 2: Previous location estimate is not well-localized

Issue: Sample degradation

Why don’t we tell where samples should be generated?


Proposal orientation tracking based monte carlo localization otmcl

Proposal: Orientation Tracking-based Monte Carlo Localization (OTMCL)


Sensor bias

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)


Analysis convergence time

Analysis – Convergence time

relative to communication range

stabilization phase

~ 7m

OTMCL achieves a decent performance even when the inertial sensor is under heavy bias


Analysis communication overhead

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


Analysis anchor density

Analysis – Anchor density

OTMCL is robust even when the anchor network is sparse


Analysis speed variance

Analysis – Speed variance

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


Analysis communication irregularity

Analysis – Communication Irregularity

OTMCL is robust to radio irregularity. Dead reckoning is responsible for maintaining accuracy


Conclusion

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


Thank you for your attention

Thank you for your attention

[email protected]


Appendix

appendix


Otmcl necessary number of samples

OTMCL: Necessary number of samples

Estimate error fairly stable when N > 50


Analysis regular node density

Analysis – Regular node density

OTMCL is robust even when the anchor network is sparse


Is it feasible on computational overhead

Is it feasible? (On computational overhead)

  • Impact of sampling (trials until fill sample set)


Radio model

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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