Transcript Test generation based on Control and Data Dependencies

Localization in Wireless Sensor
Networks
Shafagh Alikhani
ELG 7178
Fall 2008
Outline
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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
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Environments of Deployment
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Indoor vs outdoor
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Stationary vs mobile
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2D vs 3D
Localization
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What?
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–
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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
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Defining a coordinate system
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Calculating the distance between
sensor nodes
Defining a Coordinate System
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Global
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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
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Centralized vs Distributed
Anchor-free vs Anchor-based
Range-free vs Range-based
Mobile vs Stationary
Centralized vs Distributed
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Centralized
–
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All computation is done in a central server
Distributed
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Computation is distributed among the nodes
Anchor-Free vs Anchor-Based
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Anchor Nodes:
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Anchor-free
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Nodes that know their coordinates a priori
By use of GPS or manual placement
For 2D three and 3D four anchor nodes are needed
Relative coordinates
Anchor-based
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Use anchor nodes to calculate global coordinates
Range-Free vs Range-Based
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Range-Free
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Local Techniques
Hop-Counting Techniques
Range-Based
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Received Signal Strength Indicator (RSSI)
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Time of Arrival (ToA)
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–
time of flight
Time Difference of Arrival (TDoA)
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–
Attenuation
RF signal
requires time synchronization
electromagnetic (light, RF, microwave)
sound (acoustic, ultrasound)
Angle of Arrival (AoA)
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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
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Three algorithms
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Sum-dist
DV-Hop
Euclidean
Anchors
–
flood network
with their
own position
Sum-dist
Phase 1:
B
Anchors
–
flood network with own
position
10
6
Nodes
–
–
add hop distances
requires range
measurement
8
8
7
A
A: 8
B: 10+6 = 16
C: 7+8+6 = 21
C
DV-hop
Phase 1:
A-B: 15
3 hops
avg hop: 5
Anchors
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–
flood network with
own position
flood network with
avg hop distance
–
–
count number
of hops to anchors
multiply with avg hop
distance
4
1
2
1
1
Nodes
B
3
A
3
2
1
2
C
4
Euclidean
Phase 1:
Anchors
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B
flood network with
own position
C
Nodes
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determine distance by
1. range
measurement
2. geometric calculation
A
Euclidean
Phase 1:
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Needs high connectivity
Error prone (selecting wrong distance)
Perfect accuracy possible
Phase 2:
Determining Position
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Trilateration
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uses multiple distance
measurements between
known points
Must solve a set of
linear equation
C
A
B
Triangulation
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B
Law of sines: (sin a)/A=(sin b)/B=(sin c)/C
c
a
b
C
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Min-max
A
Phase 2:
Min-max
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Distance to anchors
determines a bounding
box
C
A
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Center of box estimates
node position
B
Phase 3:
Iterative refinement
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Node obtains initial position (phase 1 and 2)
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Node broadcasts its position
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Position is refined iteratively using:
–
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distances to neighbours
node’s previous positions
Phase 3:
Iterative refinement
1. Initial estimate
2. Receive neighbour
positions
3. Local lateration
4. Broadcast new
position to
neighbors
A
Monte Carlo Localization for Mobile
Nodes
Initialization: Node has no knowledge of its location.
L0 = { set of N random locations in the deployment area }
Iteration Step:
Compute new possible location set Lt based on Lt-1, the
possible location set from the previous time step, and
the new observations.
Phase 1:
Initialization
Node’s actual position
Initialization: Node has no knowledge of its location.
L0 = { set of N random locations in the deployment area }
Phase 2:
Prediction & Filtering
Node’s actual position
r
Anchor node:
Knows its own
location and
transmits it
Prediction: Node predicts its new possible locations based on previous
possible locations and given maximum velocity
Filtering: Samples inconsistent with observations are filtered out
Observations
Direct Anchor
If node hears an anchor,
the node must lie on a circle
with radius r of
the anchor’s location
Indirect Anchor
If node does not hear an anchor,
but one of its neighbors does, node
must be within distance (r, 2r] of
that anchor’s location.
r
2r
S
S
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, 2r] 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.