Localization in Wireless Sensor
Networks
Shafagh Alikhani
ELG 7178
Fall 2008
Outline
Wireless Sensor Networks
Localization
–
What? Why?
Classification of Localization Algorithms
Examples of Localization Techniques
Wireless Sensor Networks
a large number of
self

sufficient nodes
nodes have
sensing capabilities
can perform
simple computations
can communicate
with each other
Environments of Deployment
Indoor vs outdoor
Stationary vs mobile
2D vs 3D
Localization
What?
–
To determine the physical coordinates of a group of sensor
nodes in a wireless sensor network (WSN)
–
Due to application context and massive scale, use of GPS
is unrealistic, therefore, sensors need to self

organize a
coordinate system
Why?
–
To report data that is geographically meaningful
–
Services such as routing rely on location information;
geographic routing protocols; context

based routing
protocols, location

aware services
Problem Formulation
Defining a coordinate system
Calculating the distance between
sensor nodes
Defining a Coordinate System
Global
–
Aligned with some externally meaningful system
(e.g., GPS)
Relative
–
An arbitrary rigid transformation (rotation,
reflection, translation) away from the global
coordinate system
Classifications of Localization Methods
Centralized vs Distributed
Anchor

free vs Anchor

based
Range

free vs Range

based
Mobile vs Stationary
Centralized vs Distributed
Centralized
–
All computation is done in a central server
Distributed
–
Computation is distributed among the nodes
Anchor

Free vs Anchor

Based
Anchor Nodes:
–
Nodes that know their coordinates a priori
–
By use of GPS or manual placement
–
For 2D three and 3D four anchor nodes are needed
Anchor

free
–
Relative coordinates
Anchor

based
–
Use anchor nodes to calculate global coordinates
Range

Free vs Range

Based
Range

Free
–
Local Techniques
–
Hop

Counting Techniques
Range

Based
–
Received Signal Strength Indicator (RSSI)
Attenuation
RF signal
–
Time of Arrival (ToA)
time of flight
–
Time Difference of Arrival (TDoA)
requires time synchronization
electromagnetic (light, RF, microwave)
sound (acoustic, ultrasound)
–
Angle of Arrival (AoA)
RF signal
Generic Approach Using Anchor
Nodes
1. Determine the distances between regular nodes and
anchor nodes.
(Communication)
2.
Derive the position of each node from its anchor
distances.
(Computation)
3.
Iteratively
refine node positions using range information
and positions of neighboring nodes.
(Communication &
Computation)
Phase 1:
Calculating Distance to
Anchor Nodes
Three algorithms
–
Sum

dist
–
DV

Hop
–
Euclidean
Anchors
–
flood network
with
their
own position
Anchors
–
flood network with own
position
Nodes
–
add hop distances
–
requires range
measurement
Sum

dist
Phase 1:
C
A
B
A: 8
8
B: 10+6 = 16
10
6
C: 7+8+6 = 21
8
7
Anchors
–
flood network with
own position
–
flood network with
avg hop distance
Nodes
–
count number
of hops to anchors
–
multiply with avg hop
distance
DV

hop
Phase 1:
C
A
B
1
1
1
1
2
2
2
3
3
4
4
A

B: 15
3 hops
avg hop: 5
Anchors
–
flood network with
own position
Nodes
–
determine distance by
1.
range measurement
2.
geometric calculation
Euclidean
Phase 1:
C
A
B
Euclidean
Phase 1:
Needs high connectivity
Error prone (selecting wrong distance)
Perfect accuracy possible
Phase 2:
Determining Position
Trilateration
–
uses multiple distance
measurements between
known points
–
Must solve a set of
linear equation
Triangulation
–
Law of sines: (sin a)/A=(sin b)/B=(sin c)/C
Min

max
A
B
C
a
b
c
B
A
C
Phase 2:
Min

max
Distance to anchors
determines a bounding
box
Center of box estimates
node position
A
B
C
Phase
3
:
Iterative refinement
Node obtains initial position
(phase 1 and 2)
Node broadcasts its positio
n
Position is refined iteratively using:
–
distances to neighbours
–
node’s previous positions
Phase 3:
Iterative refinement
1. Initial estimate
A
2. Receive neighbour
positions
4. Broadcast new
position to
neighbors
3. Local lateration
Monte Carlo Localization for Mobile
Nodes
Initialization:
Node has no knowledge of its location.
L
0
= { set of
N
random locations in the deployment area }
Iteration Step:
Compute new possible location set
L
t
based on
L
t

1
, the
possible location set from the previous time step, and
the new observations.
Phase 1:
Initialization
Initialization:
Node has no knowledge of its location.
L
0
= { set of
N
random locations in the deployment area }
Node’s actual position
Phase 2:
Prediction & Filtering
Node’s actual position
Prediction:
Node predicts its new possible locations based on previous
possible locations and given maximum velocity
Filtering:
Samples inconsistent with observations are filtered out
Anchor node:
Knows its own
location and
transmits it
r
Observations
Indirect Anchor
If node does not hear an anchor,
but one of its neighbors does, node
must be within distance (
r
, 2
r
] of
that anchor’s location.
Direct Anchor
If node hears an anchor,
the node must lie on a circle
with radius
r
of
the anchor’s location
S
S
r
2r
Questions
1

What are the main differences between range

free and range

based
methods?
Range

based methods require extra hardware therefore have a higher cost but provide
more accurate distance measurements, whereas range

free methods use only
connectivity information and so are less accurate.
2

What are the generic steps in calculating node position using
anchor nodes?
1. Determine the distances between regular nodes and anchor nodes.
2.
Derive the position of each node from its anchor distances.
3.
Iteratively
refine node positions using range information and positions of neighboring
nodes.
3

What are the observations used for filtering the samples in the
MCL algorithm.
If node hears an anchor, the node must lie on a circle with radius
r
of the anchor’s
location.
If node does not hear an anchor, but one of its neighbors does, node must be
within distance (
r
, 2
r
] of that anchor’s location.
References
[1] I. Stojmenovic,
Handbook of Sensor Networks: Algorithms and Architectures
, Wiley Interscience, 2005.
[2] K. Langendoen and N. Reijers, "Distributed Localization in Wireless Sensor Networks: A Quantitative
Comparison“ Computer Networks (Elsevier), special issue on Wireless Sensor Networks, November 2003.
[3] E. Stevens

Navarro, V. Vivekanandan, and V.W.S. Wong, “Dual and Mixture Monte Carlo Localization
Algorithms for Mobile Wireless Sensor Networks,” in
Proceedings of
IEEE Wireless Communications and
Networking Conference
(WCNC)
, pp. 4024
–
4028, March 2007.
[4] Y. Shang and W. Ruml, “Improved MDS

Based Localization,” in
Proceedings of IEEE INFOCOM
, 2004.
[5] D. Niculescu and B. Nath, “DV Based Positioning in Ad hoc Networks,”
Kluwer Journal of
Telecommunication Systems
. 2003.
[6] L. Hu, and D. Evans, “Localization for Mobile Sensor Networks,” in
Proceeding of Tenth Annual International
Conference on Mobile Computing and Networking
(
MobiCom
2004), October 2004.
[7] Y. Shang, W. Ruml, Y. Zhang, M. Fromherz, “Localization from Mere Connectivity,” in
Proceedings of ACM
MobiHoc 2003
. June 2003.
[8] Y. Shang, W. Ruml, Y. Zhang, M. Fromherz, “Localization from Connectivity in Sensor Networks,”
IEEE
Transactions on Parallel and Distributed Systems
, vol. 15, no. 11, pp. 961

974, November 2004.
[9] A. Savvides, W. Garber, S. Adlakha, R. Moses, and M.B. Srivastava, “On the Error Characteristics of
Multihop Node Localization in Ad

Hoc Sensor Networks,“
Proceedings of the Second International
Workshop on Information Processing in Sensor Networks
(IPSN'03), pp. 317

332, April 2003.
[10] A. Savvides, H. Park and M.B. Srivastava, "The N

Hop Multilateration Primitive for Node Localization
Problems,",
ACM Mobile Networks and Applications (
Special Issue on Wireless Sensor Networks and
Applications), pp. 443

451, 2003.
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