Localization for Mobile Sensor Networks

Localization for Mobile Sensor Networks

ACM MobiCom 2004ACM MobiCom 2004Philadelphia, PAPhiladelphia, PA28 September 200428 September 2004

University of VirginiaComputer Science

Lingxuan Hu and David Evans

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www.cs.virginia.edu/mcl 2

Location Matters

• Sensor Net Applications– Mapping– Environment monitoring– Event tracking

• Geographic routing protocols


Determining Location• Direct approaches

– GPS• Expensive (cost, size, energy)• Only works outdoors, on Earth

– Configured manually• Expensive• Not possible for ad hoc, mobile networks

• Indirect approaches– Small number of seed nodes

• Seeds are configured or have GPS

– Other nodes determine location based on messages received


Hop-Count TechniquesDV-HOP [Niculescu & Nath, 2003]Amorphous [Nagpal et. al, 2003]

Works well with a few, well-located seeds and regular, static node distribution. Works poorly if nodes move or are unevenly distributed.

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Local TechniquesCentroid [Bulusu, Heidemann, Estrin, 2000]:Calculate center of all heard seed locations

APIT [He, et. al, Mobicom 2003]:Use triangular regionsDepend on a high density of

seeds (with long transmission ranges)


Our Goal

• (Reasonably) Accurate Localization in Mobile Networks

• Low Density, Arbitrarily Placed Seeds

• Range-free: no special hardware • Low communication (limited

addition to normal neighbor discovery)


Scenarios

NASA Mars TumbleweedImage by Jeff Antol

Nodes moving, seeds stationary

Nodes and seeds moving

Nodes stationary, seeds moving


Our Approach: Monte Carlo Localization

• Adapts an approach from robotics localization

• Take advantage of mobility:– Moving makes things harder…but

provides more information– Properties of time and space limit

possible locations; cooperation from neighbors

Frank Dellaert, Dieter Fox, Wolfram Burgard and Sebastian Thrun. Monte Carlo Localization for Mobile Robots. ICRA 1999.


MCL: Initialization

Initialization: Node has no knowledge of its location.

L0 = { set of N random locations in the deployment area }

Node’s actual position


MCL Step: Predict


Predict: Node guesses new possible locations based on previous possible locations and maximum velocity, vmax


Prediction

p(lt | lt-1) = c if d(lt, lt-1) < vmax

0 if d(lt, lt-1) ≥ vmax

Assumes node is equally likely to move in any direction with any speed between 0 and vmax.

Can adjust probability distribution if more is known.


MCL Step: Predict


Predict: Node guesses new possible locations based on previous possible locations and maximum velocity, vmax

Filter

Filter: Remove samples that are inconsistent with observations

Seed node: knowsand transmits location

r


Filtering

Indirect SeedIf node doesn’t hear a seed, but one of your neighbors hears it, node must be within distance (r, 2r] of that seed’s location.

Direct SeedIf node hears a seed,the node must (likely) bewith distance r ofthe seed’s location

S S


Resampling

Use prediction distribution to create enough sample points that are consistent with the observations.


Recap: AlgorithmInitialization: Node has no knowledge of its location. L0 = { set of N random locations in the deployment area }

Iteration Step: Compute new possible location set Lt based on Lt-1, thepossible location set from the previous time step, and the new observations. Lt = { } while (size (Lt) < N) do R = { l | l is selected from the prediction distribution } Rfiltered = { l | l where l R and filtering condition is met } Lt = choose (Lt Rfiltered, N)


Results Summary• Effect of network parameters:

– Speed of nodes and seeds– Density of nodes and seeds

• Cost Tradeoffs:– Memory v. Accuracy: Number of samples– Communication v. Accuracy: Indirect seeds

• Radio Irregularity: fairly resilient• Movement: control helps; group motion

hurts


Convergence

Node density nd = 10, seed density sd = 1

The localization error converges in first 10-20 steps

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Est

imate

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Time (steps)

vmax=.2 r, smax=0

vmax=r, smax=0

vmax=r, smax=r


Speed Helps and Hurts

Increasing speed increases location uncertainty ̶R but provides more observations.

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rror

(r)

vmax (r distances per time unit)

sd=1, smin=0, smax=vmax

sd=1, smax=smin=r

sd=2, smax=vmax

sd=2, smax=smin=r

Node density nd = 10


00.20.40.60.81

1.21.41.61.82

2.22.42.62.83

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imate

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Seed Density

MCL

Centroid

Amorphous

Seed Density

nd = 10, vmax = smax=.2r

Better accuracy than other localization algorithms over range of seed densities

Centroid: Bulusu, Heidemann and Estrin. IEEE Personal Communications Magazine. Oct 2000.

Amorphous: Nagpal, Shrobe and Bachrach. IPSN 2003.


Cost Tradeoff: Samples Maintained

00.10.20.30.40.50.60.70.80.91.0

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imate

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Sample Size (N)

sd=1, vmax=smax=.2r

sd=1, vmax=smax=r

sd=2, vmax=smax=.2rsd=2, vmax=smax=r

1.1 nd = 10

Good accuracy is achieved with only 20 samples (~100 bytes)


Cost Tradeoff: Impact of Indirect Seeds

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imate

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Seed Density

Direct seeds onlyDirect and Indirect seeds

Indirect seeds help, and cost is low if neighbor discovery is required.

nd = 10, vmax = smax=.2r


Radio Irregularity

nd = 10, sd = 1, vmax = smax=.2r

Insensitive to irregular radio pattern

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imate

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Degree of Irregularity (r varies ±dr)

MCL

Centroid

Amorphous


Motion

nd=10, vmax=smax=r

Adversely affected by consistent group motion

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Maximum Group Motion Speed (r units per time step)

sd =.3

sd =1

sd =2

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0 20 40 60 80 100 120 140 160 180 200Est

imate

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Time

Random, vmax=smax=.2r

Area Scan

Random, vmax=0, smax=.2r

Scan

Stream and Currents Random Waypoint vs. Area Scan

Controlled motion of seeds improves accuracy


Future Work: Security• Attacks on localization:

– Bogus seed announcements• Require authentication between seeds and

nodes

– Bogus indirect announcements• Retransmit tokens received from seeds

– Replay, wormhole attacks• Filtering has advantages as long as you get

one legitimate announcement

• Proving node location to others


Summary• Mobility can improve localization:

– Increases uncertainty, but more observations

• Monte Carlo Localization– Maintain set of samples representing

possible locations– Filter out impossible locations based on

observations from direct and indirect seeds– Achieves accurate localization cheaply with

low seed density


Thanks!

http://www.cs.virginia.edu/mcl

People: Tarek Abdelzaher, Tian He, Anita Jones, Brad Karp, Kenneth Lodding, Nathaneal Paul, Yinlin Yang, Joel Winstead, Chalermpong Worawannotai

Funding: NSF ITR, NSF CAREER, DARPA SRS

Documents

Localization for Mobile Sensor Networks