© 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
© 2007 Sean A. Williams2
Overview• Background on Localization
• Introduction to Ecolocation– Novelty and Contribution
• Paper Details
• Results
• Conclusions
© 2007 Sean A. Williams3
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
© 2007 Sean A. Williams4
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
© 2007 Sean A. Williams5
Introduction• Error COntrolling LOCAlizaTION
– AKA: ECOLOCATION
• Motivation– To provide a localization technique– Outperforms various other localization
methods– Robust (fluctuation of Received Signal
Strength –RSS)
© 2007 Sean A. Williams6
Introduction• Novelty of Ecolocation
– Distance-based ordering of reference nodes creates a unique fingerprint in a region
© 2007 Sean A. Williams7
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
© 2007 Sean A. Williams8
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
© 2007 Sean A. Williams9
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)
© 2007 Sean A. Williams10
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
© 2007 Sean A. Williams11
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
© 2007 Sean A. Williams12
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
© 2007 Sean A. Williams13
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
X-AXIS (length units)
Y-A
XIS
(le
ng
th u
nits
)
Location esitmate for 123745968
P
EA1
A2
A3
A7
A4 A5
A9
A6
A8
© 2007 Sean A. Williams14
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
X-AXIS (length units)
Y-A
XIS
(le
ngth
un
its)
Location estimate for 124739586
P
EA1
A2
A4
A7
A3 A9
A5
A8
A6
© 2007 Sean A. Williams15
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
X-AXIS (length units)
Y-A
XIS
(le
ng
th u
nits
)
Location estimate for 913276584
P
E
A9
A1
A3
A2
A7 A6
A5
A8
A4
© 2007 Sean A. Williams16
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
© 2007 Sean A. Williams17
SIMULATION RESULTS
© 2007 Sean A. Williams18
Location Error
© 2007 Sean A. Williams19
Location Precision• Standard Deviation in Location Error
© 2007 Sean A. Williams20
EXPERIMENTAL RESULTS
© 2007 Sean A. Williams21
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
© 2007 Sean A. Williams22
True vs. Ecolocation
© 2007 Sean A. Williams23
Parking Lot Location Error
© 2007 Sean A. Williams24
True vs. Ecolocation
© 2007 Sean A. Williams25
Indoor Location Error
© 2007 Sean A. Williams26
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.
© 2007 Sean A. Williams27
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
© 2007 Sean A. Williams28
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
© 2007 Sean A. Williams29
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