Localization in Wireless Sensor

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Nov 21, 2013 (3 years and 6 months ago)

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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.