© 2007 Sean A. Williams 1 Ecolocation: A Sequence Based Technique for RF Localization in Wireless...

© 2007 Sean A. Williams1

Ecolocation: A Sequence Based Technique for RF

Localization in Wireless Sensor Networks

Authors: Kiran Yedavalli, Bhaskar Krishnamachari, Sharmila Ravula, Bhaskar Srinivasan

Paper Presented By: Sean A. Williams

Mobile Computing


Overview• Background on Localization

• Introduction to Ecolocation– Novelty and Contribution

• Paper Details

• Results

• Conclusions


Background• Localization is process of determining an

entity’s spatial coordinates.• Advantages of localization for WSNs?

– Locating disasters and fires– Locating enemies on battlefields– Other services for rescue and relief

• Range-based vs. Range-free– Estimating distance btw unknown & reference

to determine location– Estimating distance of unknown node w/o

reference to determine location


Background• Some Localization Techniques

– Proximity• Closest reference node

= location of unknown node

– Centroid• Center of all reference nodes in range

– Approximate point in Triangle• Creates triangle of each 3 anchor combination,

location is the intersection of the triangles

– Maximum Likelihood Estimation• Statistical estimation technique

APIT


Introduction• Error COntrolling LOCAlizaTION

– AKA: ECOLOCATION

• Motivation– To provide a localization technique– Outperforms various other localization

methods– Robust (fluctuation of Received Signal

Strength –RSS)


Introduction• Novelty of Ecolocation

– Distance-based ordering of reference nodes creates a unique fingerprint in a region


Evaluation Scenarios• Ideal vs. Real World Scenarios

• Ideal: – Without multi-path fading and shadowing– Received Signal Strength (RSS) represents

distance– Low RSS = Farther away

• Real:– With multi-path fading and shadowing– Low RSS ~ Farther away


Ecolocation (Ideal)• Location based on

unknown nodes constraints and grid-point location constraints

• Unknown Node constraints determined by:– (RSS) of reference nodes

and rank sequentially.– Based on the number of

reference nodes and there RSS from the unknown node

B:1 C:2 D:3 E:4 F:5

R1 R2<R1 R3<R1 R4<R1 R5<R1

R3<R2 R4<R2 R5<R2

R4<R3 R5<R3

R5<R4


Ecolocation (Ideal)• Location Grid points constraints

– based on the Euclidean Distance to the reference nodes and not the RSS

• Overall location of unknown node is:– Compare unknown constraints with all

location grid point constraints– Grid point that has most matches is LE

Given Points: P=(p1…pn) & Q=(q1…qn)


Ecolocation (Real)• LE is effected by shadowing and fading

• Ecolocation is robust to multi-path effects– Evident in display of various erroneous constraints on

an unknown node


Algorithm• Generate constraint matrix A

– Based on Euclidean Distance btw grid points and reference nodes

• Generate constraint matrix B– Based on RSS btw unknown node & reference node

• If A’s element matches B’s -> increment maxConstraint count

• Find all grid points where maximum number of constraints are matched

• LE = centroid of those matching gridpoints


No Errors

0 1 2 3 4 5 6 7 8 9 10 11 12

1

2

3

4

5

6

7

8

9

10

11

12

X-AXIS (length units)

Y-A

XIS

(le

ng

th u

nits

)

Location estimate for 123456789

E

P

A1

A2

A3

A4

A5 A6

A7

A8

A9


13.9% Erroneous Constraints

0 1 2 3 4 5 6 7 8 9 10 11 12

1

2

3

4

5

6

7

8

9

10

11

12


Y-A

XIS

(le

ng

th u

nits

)

Location esitmate for 123745968

P

EA1

A2

A3

A7

A4 A5

A9

A6

A8


22.2 % Erroneous Constraints

0 1 2 3 4 5 6 7 8 9 10 11 12

1

2

3

4

5

6

7

8

9

10

11

12


Y-A

XIS

(le

ngth

un

its)


P

EA1

A2

A4

A7

A3 A9

A5

A8

A6


47.2% Erroneous Constraints

0 1 2 3 4 5 6 7 8 9 10 11 12

1

2

3

4

5

6

7

8

9

10

11

12


Y-A

XIS

(le

ng

th u

nits

)


P

E

A9

A1

A3

A2

A7 A6

A5

A8

A4


Evaluation• Simulation model equation

• Simulation parameters and characteristics

randomly placed Nodes

resolution Scanning

density node Reference

nodes reference ofNumber

tsmeasuremenRSSindeviationstandard

),0(,valuerandomGaussian

ExponentPathloss

distanceofPathloss

PowerTransmit

2

0

NX

dPL

PT • 100 Random Trials

• 10 seeds, 48bit RNG


SIMULATION RESULTS


Location Error


Location Precision• Standard Deviation in Location Error


EXPERIMENTAL RESULTS


Real World Experiments• Parking Lot

– 11 reference MICA 2 Motes within 1 hop– No NLOS– Motes record RSS of each other that

broadcast– Location Estimated and compared to actual

• Office Building– 12 reference MICA 2 Motes within 1 hop– NLOS– Included power attenuation based on walls


True vs. Ecolocation


Parking Lot Location Error


True vs. Ecolocation


Indoor Location Error


Summary• Localization Techniques are more

accurate in:– more open outdoor environments– NLOS

• Possible to create a hybrid of localization techniques.– Taking advantage of different methods based

on RF Techniques– TDOA/AOA, TOA/RSS, TDOA/RSS,

RSS/Proximity, etc.


Critique• Strengths

– The idea is logical and novel– Evaluation is thorough (Simulation, Indoor,

Outdoor)

• Weakness– Some details are left out, making it unclear– How are % calculated?– Why decrement 1 if not a match? Why not do

nothing?– Flow of paper

• Related work usually in beginning or end


Relativity• Course Relativity

– We have discussed many location techniques– Both Range-based and Range-free– Range estimations based on RSS information

• Project Relativity– We are performing a site survey tool which

utilizes the RSS information


References• Kiran Yedavalli, et al. “Ecolocation: a sequence based technique for

RF localization in wireless sensor networks”. Fourth Internation Symposium on Information Processing Sensor Networks, 2005. Pages 285-292.

• Thoedore S. Rappaport, Wireless Communication, Principles & Practice, Prentice hall, 1999.

• ceng.usc.edu/~bkrishna/research/talks/Krishnamachari_AROWorkshop05_Localization.ppt

© 2007 Sean A. Williams 1 Ecolocation: A Sequence Based Technique for RF Localization in Wireless...

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