Localization for Mobile Sensor Networks

ACM MobiCom 2004ACM MobiCom 2004

Lingxuan Hu David Evans

Department of Computer Science

University of Virginia

Localization

• Location Awareness

• Importance– Environment monitoring– VehicleTracking– Location based routing – save significant

energy– Improve caching behavior– Security enhanced (wormhole attacks)

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

– Dependence on special hardware– Requirement for particular network topologies

Hop-Count Techniques

DV-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.

r

1

1

2

23

3

33

4

4

4

44

5

5

6

7

8

Local Techniques

Centroid [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)

Environment considered

• Conditions– No special hardware for ranging is

available– The prior deployment of seed (beacons)

nodes is unknown– The seed density is low– The node distribution is irregular– Nodes and seeds can move

uncontrollably.

Scenarios

NASA Mars TumbleweedImage by Jeff Antol

Nodes moving, seeds stationary

Nodes and seeds moving

Nodes stationary, seeds moving

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

Node’s actual position

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

Prediction

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: Filter

Node’s actual position

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.

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

0

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0.8

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1.2

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0 5 10 15 20 25 30 35 40 45 50

Est

imate

Err

or

(r)

Time (steps)

vmax=.2 r, smax=0

vmax=r, smax=0

vmax=r, smax=r

00.20.40.60.81

1.21.41.61.82

2.22.42.62.83

0.1 0.5 1 1.5 2 2.5 3 3.5 4

Est

imate

Err

or

(r)

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.

Radio Irregularity

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

Insensitive to irregular radio pattern

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1.4

1.6

1.8

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0 0.1 0.2 0.3 0.4 0.5

Est

imate

Err

or

(r)

Degree of Irregularity (r varies ±dr)

MCL

Centroid

Amorphous

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

THANK YOU